Publish party-position estimates and validation materials

validation/CASE_SELECTION.md

@@ -0,0 +1,37 @@
+# Predeclared case-selection rules
+
+These rules were recorded before inspecting the selected parties' position estimates.
+
+## Trajectory figure
+
+The figure will use parties selected for cross-national recognition, family diversity, long election coverage, and relevance to interpreting movement on the two scales. Selection is not based on the magnitude of the estimated movement.
+
+Preselected parties:
+
+- Germany: Social Democratic Party of Germany (PartyFacts 383)
+- Germany: Christian Democratic Union (PartyFacts 1375)
+- United Kingdom: Labour Party (PartyFacts 1516)
+- United Kingdom: Conservative Party (PartyFacts 1567)
+- Denmark: Social Democratic Party, short name SD (PartyFacts 379)
+- Sweden: Sweden Democrats (PartyFacts 409)
+- United States: Democratic Party (PartyFacts 432)
+- United States: Republican Party (PartyFacts 809)
+
+The final display may use a subset only to preserve legibility, but exclusions must be based on panel layout or insufficient coverage rather than unattractive results. Both posterior means and 95% latent-position credible intervals will be shown. Text will not infer a cause of movement from the estimates alone.
+
+## Two-dimensional landmarks
+
+Landmarks will be selected from recognizable party-election cases, with representation of economically left/right, culturally cosmopolitan/traditionalist, and moderate combinations. Target years are declared before checking exact positions; if the requested year is absent, the nearest election within three years will be used and disclosed.
+
+- German Social Democratic Party: 1972 and 2021
+- German Christian Democratic Union: 1983 and 2021
+- UK Labour Party: 1983 and 1997
+- UK Conservative Party: 1979 and 2019
+- Swedish Social Democratic Labour Party (PartyFacts 487): 1994 and 2022
+- Sweden Democrats: 2010 and 2022
+- French National Front (PartyFacts 433): 1988 and 2022
+- German The Left (PartyFacts 1545): 2021
+- US Democratic Party: 2020
+- US Republican Party: 2020
+
+Labels may be pruned only to avoid overlap. The plotting-data table will retain all declared cases, including missing cases and substitutions.

validation/EVIDENCE_MANIFEST.csv

@@ -0,0 +1,13 @@
+id,topic,input,output,status
+1,country coverage,release v0 election-year panel,metadata/country_coverage_v0.csv,complete
+2,scale trajectories,release v0 election-year panel,validation/figures/party_trajectories.pdf,complete
+3,landmark party space,release v0 election-year panel,validation/figures/party_landmarks.pdf,complete
+4,research workflow,documented source and release workflow,validation/figures/research_workflow.pdf,complete
+5,party-blocked validation,guarded blocked fit with 82 parties and completed post-estimation extraction,validation/outputs/blocked_validation_summary.csv,complete_with_convergence_limitation
+6,predictive coverage,production posterior run run_2026-06-12_09-34-03,validation/outputs/ppc/posterior_predictive_by_dimension_item.csv,complete
+7,predictive calibration curve,production posterior run run_2026-06-12_09-34-03,validation/outputs/ppc/posterior_predictive_calibration_curve.csv,complete
+8,predictive residual patterns,production posterior predictive observations,validation/outputs/ppc/posterior_predictive_residual_patterns.csv,complete
+9,V-Party sensitivity,completed no-V-Party fit ending 2026-06-05_14-57-17,validation/outputs/vparty_sensitivity_groups.csv,complete
+10,V-Party subgroup sensitivity,completed no-V-Party fit ending 2026-06-05_14-57-17,validation/outputs/vparty_sensitivity_groups.csv,complete
+11,pooled source-support balance,release v0 election-year panel,validation/outputs/source_support_pooled.csv,complete
+12,partially pooled source-support heterogeneity,release v0 election-year panel,validation/outputs/source_support_interactions.csv,complete

validation/README.md

@@ -0,0 +1,30 @@
+# Validation materials
+
+This directory contains reproducible validation code, party-blocked sensitivity analyses, posterior predictive checks, subgroup balance diagnostics, and figure-generation scripts for the party-position panel.
+
+All analyses preserve the production release (`v0`, production run `run_2026-06-12_09-34-03`). Existing posterior draws and completed sensitivity fits are reused wherever possible. The party-blocked expert validation tests prediction for parties whose expert evidence is entirely excluded from estimation.
+
+## Inputs
+
+- `data/releases/party_2d_election_year_panel_v0.csv.gz`
+- `data/releases/party_2d_annual_model_output_v0.csv.gz`
+- production posterior run `run_2026-06-12_09-34-03`
+- completed no-V-Party sensitivity output `party_positions_2026-06-05_14-57-17.csv`
+- model-ready input files in `data/`
+
+Large model runs and their chains remain outside Git. This package contains the scripts, manifests, diagnostics, grouped summaries, plotting data, and figures needed to inspect and reproduce the published validation results. Two large row-level intermediate tables used during post-estimation are intentionally excluded; the retained grouped summaries and scripts document all reported calculations.
+
+## Computational notes
+
+The validation fit retains the production warmup length and four-chain design but uses 1,000 retained iterations per chain (4,000 draws total), rather than 2,000 for the production release. The scripts record immutable train/test identifiers and input hashes before fitting and use existing posterior draws for descriptive and posterior-predictive analyses where applicable.
+
+## Reproduction Entry Points
+
+- `run_postprocessing.R`: country coverage, scale illustrations, V-Party sensitivity and partially pooled source-support diagnostics.
+- `validate_uncertainty.jl`: production-chain predictive coverage, calibration and source/item/country/decade breakdowns.
+- `prepare_blocked_validation.jl`: deterministic party-blocked train/test construction.
+- `run_blocked_validation.sh`: guarded low-priority fit with disk, duplicate-process, toolchain and logging checks.
+- `monitor_long_run.sh`: non-invasive status, child-process, disk and recent-log monitoring.
+- `finalize_blocked_validation.sh`: guarded post-estimation, held-out summaries and compact diagnostic-manifest collection after the fit succeeds.
+- `summarize_blocked_validation.jl`: held-out prediction records and grouped performance summaries after the fit.
+- `plot_workflow.R`: data-processing workflow schematic.

validation/finalize_blocked_validation.sh

@@ -0,0 +1,68 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd -P)"
+cd "$repo_root"
+
+if [[ "${PARTY2D_APPROVE_POSTESTIMATION:-}" != "YES" ]]; then
+  echo "Refusing to read chains or run post-estimation without explicit approval." >&2
+  echo "After approval, rerun with PARTY2D_APPROVE_POSTESTIMATION=YES." >&2
+  exit 64
+fi
+
+run_dir="${1:-$repo_root/_local/validation/blocked_party}"
+summary_dir="${2:-$repo_root/validation/outputs}"
+estimation_dir="$run_dir/estimations"
+
+if [[ ! -f "$run_dir/model_fit.complete" ]]; then
+  echo "Refusing to finalize: the guarded fit is not marked complete." >&2
+  exit 1
+fi
+if [[ ! -f "$run_dir/model_fit.exit_code" ]] || [[ "$(tr -dc '0-9' < "$run_dir/model_fit.exit_code")" != "0" ]]; then
+  echo "Refusing to finalize: the guarded fit has no successful exit code." >&2
+  exit 1
+fi
+
+mapfile -t model_runs < <(find "$run_dir/model_run/latest" -mindepth 1 -maxdepth 1 -type d -name 'run_*' 2>/dev/null | sort)
+if [[ "${#model_runs[@]}" -eq 0 ]]; then
+  # The production saver currently writes its run under the repository-level
+  # output directory even when --data-dir points at a validation workspace.
+  # Match the completed fit by timestamp instead of relying on "latest" alone.
+  fit_started="$(tr -d '\r\n' < "$run_dir/model_fit.started")"
+  fit_completed="$(tr -d '\r\n' < "$run_dir/model_fit.complete")"
+  mapfile -t model_runs < <(find "$repo_root/outputs/model_outputs/latest" -mindepth 1 -maxdepth 1 -type d -name 'run_*' -newermt "$fit_started" ! -newermt "$fit_completed" | sort)
+fi
+if [[ "${#model_runs[@]}" -ne 1 ]]; then
+  echo "Expected exactly one blocked model run; found ${#model_runs[@]}." >&2
+  printf '%s\n' "${model_runs[@]}" >&2
+  exit 1
+fi
+model_run="${model_runs[0]}"
+
+mkdir -p "$estimation_dir" "$summary_dir/blocked_fit_diagnostics"
+nice -n 10 julia --project=. src/julia/02_post_estimation.jl \
+  --run-dir "$model_run" --output-dir "$estimation_dir" \
+  2>&1 | tee "$run_dir/post_estimation.log"
+
+mapfile -t position_files < <(find "$estimation_dir" -maxdepth 1 -type f -name 'party_positions_*.csv' | sort)
+if [[ "${#position_files[@]}" -ne 1 ]]; then
+  echo "Expected exactly one blocked position file; found ${#position_files[@]}." >&2
+  exit 1
+fi
+
+julia --project=. validation/summarize_blocked_validation.jl \
+  "${position_files[0]}" "$run_dir" "$summary_dir" \
+  2>&1 | tee "$run_dir/blocked_summary.log"
+
+cp "$model_run/metadata.json" "$summary_dir/blocked_fit_diagnostics/metadata.json"
+cp "$model_run/diagnostics/run_metrics.json" "$summary_dir/blocked_fit_diagnostics/run_metrics.json"
+cp "$run_dir/blocked_validation_manifest.csv" "$summary_dir/blocked_fit_diagnostics/blocked_validation_manifest.csv"
+cp "$run_dir/blocked_validation_strata.csv" "$summary_dir/blocked_fit_diagnostics/blocked_validation_strata.csv"
+cp "$run_dir/blocked_parties.csv" "$summary_dir/blocked_fit_diagnostics/blocked_parties.csv"
+cp "$run_dir/input_sha256sums.txt" "$summary_dir/blocked_fit_diagnostics/input_sha256sums.txt"
+cp "$run_dir/model_fit.environment" "$summary_dir/blocked_fit_diagnostics/model_fit.environment"
+cp "$run_dir/stanc_version.txt" "$summary_dir/blocked_fit_diagnostics/stanc_version.txt"
+
+echo "Blocked validation finalized from: $model_run"
+echo "Position file: ${position_files[0]}"
+echo "Summary directory: $summary_dir"

validation/logs/posterior_predictive_coverage.log

@@ -0,0 +1,146 @@
+============================================================
+UNCERTAINTY VALIDATION: Posterior Predictive Coverage
+============================================================
+Following Claassen (2019) validation framework
+Posterior predictive intervals account for both position
+uncertainty AND observation-level measurement noise.
+
+Using specified run directory: /projects/party4d/archive/party2d_replication/outputs/model_outputs/latest/run_2026-06-12_09-34-03
+Loaded expert_dim.csv: 22994 observations
+  Unique rr values: 4261
+  Item indices (var_exp_dim): [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+  Dimensions (dim_idx_exp): [1, 2]
+
+Loading 4 chain files (selective columns)...
+  Need 8547 columns (4261 rr × 2 dims + 24 item params + 1 phi)
+  Found 8547/8547 columns in chains
+  Loading chain 1: chain_1.csv... 2000 samples, 39.3s
+  Loading chain 2: chain_2.csv... 2000 samples, 22.4s
+  Loading chain 3: chain_3.csv... 2000 samples, 24.5s
+  Loading chain 4: chain_4.csv... 2000 samples, 16.7s
+Combined: 8000 total posterior draws
+
+V5 detected: using Beta(phi*K*mu, phi*K*(1-mu)) with per-observation K
+Computing posterior predictive coverage (95% level)
+  Expert observations: 22994
+  Posterior draws: 8000
+
+  Progress: 5.0% (1149 / 22994)
+  Progress: 10.0% (2298 / 22994)
+  Progress: 15.0% (3447 / 22994)
+  Progress: 20.0% (4596 / 22994)
+  Progress: 25.0% (5745 / 22994)
+  Progress: 30.0% (6894 / 22994)
+  Progress: 35.0% (8043 / 22994)
+  Progress: 40.0% (9192 / 22994)
+  Progress: 45.0% (10341 / 22994)
+  Progress: 50.0% (11490 / 22994)
+  Progress: 55.0% (12639 / 22994)
+  Progress: 60.0% (13788 / 22994)
+  Progress: 65.0% (14937 / 22994)
+  Progress: 70.0% (16086 / 22994)
+  Progress: 75.0% (17235 / 22994)
+  Progress: 80.0% (18384 / 22994)
+  Progress: 84.9% (19533 / 22994)
+  Progress: 89.9% (20682 / 22994)
+  Progress: 94.9% (21831 / 22994)
+  Progress: 99.9% (22980 / 22994)
+  Progress: 100.0% (22994 / 22994)
+
+============================================================
+POSTERIOR PREDICTIVE COVERAGE (95%)
+============================================================
+
+  economic left-right                     : 89.8% [89.1%, 90.5%]  (6698/7455)
+
+  By survey source:
+    Project           N      PPC
+    V-Party        5645    87.5%
+    CHES           1177    97.3%
+    POPPA           384    98.7%
+    GPS             249    94.0%
+
+  By decade:
+    Decade        N      PPC
+    1970        658    88.1%
+    1980        712    88.9%
+    1990       1454    88.0%
+    2000       1758    89.4%
+    2010       2407    90.2%
+    2020        466    99.1%
+
+  cultural cosmopolitan--traditionalist   : 84.8% [84.2%, 85.3%]  (13170/15539)
+
+  By survey source:
+    Project           N      PPC
+    V-Party       14114    83.8%
+    CHES           1176    94.3%
+    GPS             249    91.2%
+
+  By decade:
+    Decade        N      PPC
+    1970       1633    86.2%
+    1980       1772    87.9%
+    1990       3485    85.5%
+    2000       3965    84.3%
+    2010       4426    82.1%
+    2020        258    97.7%
+Saved detailed posterior-predictive results to: revision/outputs/ppc
+
+============================================================
+RECOMPUTING WITH 80% LEVEL (Claassen comparison)
+============================================================
+
+V5 detected: using Beta(phi*K*mu, phi*K*(1-mu)) with per-observation K
+Computing posterior predictive coverage (80% level)
+  Expert observations: 22994
+  Posterior draws: 8000
+
+  Progress: 5.0% (1149 / 22994)
+  Progress: 10.0% (2298 / 22994)
+  Progress: 15.0% (3447 / 22994)
+  Progress: 20.0% (4596 / 22994)
+  Progress: 25.0% (5745 / 22994)
+  Progress: 30.0% (6894 / 22994)
+  Progress: 35.0% (8043 / 22994)
+  Progress: 40.0% (9192 / 22994)
+  Progress: 45.0% (10341 / 22994)
+  Progress: 50.0% (11490 / 22994)
+  Progress: 55.0% (12639 / 22994)
+  Progress: 60.0% (13788 / 22994)
+  Progress: 65.0% (14937 / 22994)
+  Progress: 70.0% (16086 / 22994)
+  Progress: 75.0% (17235 / 22994)
+  Progress: 80.0% (18384 / 22994)
+  Progress: 84.9% (19533 / 22994)
+  Progress: 89.9% (20682 / 22994)
+  Progress: 94.9% (21831 / 22994)
+  Progress: 99.9% (22980 / 22994)
+  Progress: 100.0% (22994 / 22994)
+
+============================================================
+POSTERIOR PREDICTIVE COVERAGE (80%)
+============================================================
+
+  economic left-right                     : 75.9% [74.9%, 76.8%]  (5655/7455)
+
+  By survey source:
+    Project           N      PPC
+    V-Party        5645    71.5%
+    CHES           1177    90.1%
+    POPPA           384    92.2%
+    GPS             249    82.7%
+
+  By decade:
+    Decade        N      PPC
+    1970        658    69.3%
+    1980        712    73.5%
+    1990       1454    73.8%
+    2000       1758    74.1%
+    2010       2407    77.0%
+    2020        466    95.7%
+
+  cultural cosmopolitan--traditionalist   : 67.9% [67.1%, 68.6%]  (10544/15539)
+
+  By survey source:
+    Project           N      PPC

validation/monitor_long_run.sh

@@ -0,0 +1,50 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+run_dir="${1:-_local/validation/blocked_party}"
+pid_file="$run_dir/model_fit.pid"
+log_file="$run_dir/model_fit.log"
+
+if [[ ! -f "$pid_file" ]]; then
+  echo "No PID file at $pid_file"
+  exit 1
+fi
+
+pid="$(tr -dc '0-9' < "$pid_file")"
+echo "Run directory: $run_dir"
+echo "PID: $pid"
+date --iso-8601=seconds
+
+if ps -p "$pid" >/dev/null 2>&1; then
+  echo "Status: running"
+  ps -o pid,ppid,etimes,%cpu,%mem,rss,vsz,stat,cmd -p "$pid"
+  echo "Model processes:"
+  julia_pid="$(pgrep -P "$pid" -f 'julia.*01_run_model[.]jl' | head -1 || true)"
+  if [[ -n "$julia_pid" ]]; then
+    process_ids="$pid,$julia_pid"
+    while IFS= read -r child_pid; do
+      [[ -n "$child_pid" ]] && process_ids="$process_ids,$child_pid"
+    done < <(pgrep -P "$julia_pid" || true)
+    ps -o pid,ppid,etimes,%cpu,%mem,rss,ni,stat,cmd -p "$process_ids"
+  else
+    ps -eo pid,ppid,etimes,%cpu,%mem,rss,ni,stat,cmd | awk -v p="$pid" '$2 == p || $1 == p'
+  fi
+else
+  echo "Status: not running"
+fi
+
+echo "Latest sampler progress:"
+grep 'Iteration:' "$log_file" 2>/dev/null | tail -12 || true
+
+echo "Recorded sampler events:"
+printf '  rejected-proposal exceptions: '
+grep -c '^Exception:' "$log_file" 2>/dev/null || true
+printf '  divergence messages: '
+grep -ci 'divergent transition' "$log_file" 2>/dev/null || true
+
+echo "Disk use:"
+du -sh "$run_dir" _local/tmp/blocked_validation 2>/dev/null || true
+df -h "$run_dir" | tail -1
+
+echo "Recent log:"
+tail -40 "$log_file" 2>/dev/null || true

validation/outputs/blocked_fit_diagnostics/blocked_parties.csv

@@ -0,0 +1,83 @@
+party_id,country,region,first_expert_year,last_expert_year,expert_period,text_years,expert_rows,lr_rows,stratum,selected
+42,HU,Europe,2010,2024,2010s,3,33,6,Europe / 2010s,true
+96,SI,Europe,1992,2019,1990s,7,31,4,Europe / 1990s,true
+172,NL,Europe,1999,1999,1990s,5,2,1,Europe / 1990s,true
+216,MX,Latin America,1991,2020,1990s,8,74,1,Latin America / 1990s,true
+232,CA,North America,1972,2000,pre-1990,18,63,0,North America / pre-1990,true
+237,LT,Europe,2004,2019,2000s,4,36,4,Europe / 2000s,true
+281,BE,Europe,1971,1974,pre-1990,6,14,2,Europe / pre-1990,true
+284,PT,Europe,1987,2024,pre-1990,9,88,9,Europe / pre-1990,true
+292,BA,Europe,2002,2019,2000s,6,37,0,Europe / 2000s,true
+298,NL,Europe,2006,2024,2000s,5,42,7,Europe / 2000s,true
+306,TR,Europe,2002,2024,2000s,5,32,1,Europe / 2000s,true
+363,IS,Europe,1971,2024,pre-1990,23,103,9,Europe / pre-1990,true
+441,ES,Europe,1977,2024,pre-1990,15,116,9,Europe / pre-1990,true
+447,IL,Other,2021,2022,2020s,4,4,2,Other / 2020s,true
+466,CZ,Europe,1992,2024,1990s,8,72,8,Europe / 1990s,true
+472,SI,Europe,1990,2024,1990s,9,72,8,Europe / 1990s,true
+487,SE,Europe,1970,2024,pre-1990,24,123,18,Europe / pre-1990,true
+500,BE,Europe,1978,2024,pre-1990,12,102,9,Europe / pre-1990,true
+537,BA,Europe,1998,2019,1990s,8,51,0,Europe / 1990s,true
+557,IL,Other,1999,2003,1990s,3,14,0,Other / 1990s,true
+633,BE,Europe,1971,2024,pre-1990,16,114,9,Europe / pre-1990,true
+705,NO,Europe,1973,2024,pre-1990,19,90,11,Europe / pre-1990,true
+714,NL,Europe,2014,2023,2010s,3,8,4,Europe / 2010s,true
+848,ES,Europe,1999,2024,1990s,15,56,8,Europe / 1990s,true
+910,HU,Europe,1990,2010,1990s,5,41,3,Europe / 1990s,true
+934,IT,Europe,1972,1992,pre-1990,14,42,7,Europe / pre-1990,true
+946,ES,Europe,1999,1999,1990s,6,2,1,Europe / 1990s,true
+950,EC,Latin America,1979,2019,pre-1990,4,100,0,Latin America / pre-1990,true
+953,IL,Other,2003,2022,2000s,4,18,2,Other / 2000s,true
+986,GB,Europe,1999,2024,1990s,7,25,9,Europe / 1990s,true
+1004,CA,North America,2004,2023,2000s,7,46,1,North America / 2000s,true
+1036,IL,Other,1973,2022,pre-1990,17,104,2,Other / pre-1990,true
+1055,IE,Europe,1973,2024,pre-1990,20,102,9,Europe / pre-1990,true
+1060,TR,Europe,1973,2024,pre-1990,15,67,1,Europe / pre-1990,true
+1072,NO,Europe,1973,2024,pre-1990,19,88,11,Europe / pre-1990,true
+1096,FI,Europe,1970,1987,pre-1990,13,42,9,Europe / pre-1990,true
+1123,CH,Europe,2023,2024,2020s,13,3,2,Europe / 2020s,true
+1126,IT,Europe,1972,2006,pre-1990,10,11,7,Europe / pre-1990,true
+1138,LU,Europe,1984,2023,pre-1990,7,48,3,Europe / pre-1990,true
+1157,NL,Europe,1977,2024,pre-1990,14,109,9,Europe / pre-1990,true
+1166,BA,Europe,1996,2014,1990s,10,49,0,Europe / 1990s,true
+1219,ZA,Other,1994,2019,1990s,6,44,0,Other / 1990s,true
+1241,MX,Latin America,2019,2020,2010s,7,4,1,Latin America / 2010s,true
+1242,SK,Europe,1994,1998,1990s,4,14,0,Europe / 1990s,true
+1249,IS,Europe,1971,1995,pre-1990,15,50,8,Europe / pre-1990,true
+1274,SE,Europe,1970,2024,pre-1990,24,114,18,Europe / pre-1990,true
+1331,MX,Latin America,2006,2020,2000s,5,25,1,Latin America / 2000s,true
+1359,PT,Europe,1975,2024,pre-1990,18,114,9,Europe / pre-1990,true
+1386,SK,Europe,2010,2024,2010s,3,33,6,Europe / 2010s,true
+1388,GB,Europe,1999,2024,1990s,9,32,9,Europe / 1990s,true
+1415,CH,Europe,2011,2011,2010s,3,7,0,Europe / 2010s,true
+1428,CA,North America,1993,2023,1990s,10,60,1,North America / 1990s,true
+1431,HR,Europe,1992,2024,1990s,9,59,5,Europe / 1990s,true
+1450,EC,Latin America,1984,2009,pre-1990,3,84,0,Latin America / pre-1990,true
+1454,BA,Europe,1996,2019,1990s,9,51,0,Europe / 1990s,true
+1459,NL,Europe,2002,2024,2000s,7,16,8,Europe / 2000s,true
+1467,NL,Europe,2010,2024,2010s,5,12,6,Europe / 2010s,true
+1508,MK,Europe,1998,2019,1990s,9,44,0,Europe / 1990s,true
+1540,AU,Asia-Pacific,1972,1972,pre-1990,10,7,0,Asia-Pacific / pre-1990,true
+1567,GB,Europe,1970,2024,pre-1990,22,109,9,Europe / pre-1990,true
+1665,BG,Europe,1991,2024,1990s,8,75,6,Europe / 1990s,true
+1673,BA,Europe,2000,2019,2000s,5,16,0,Europe / 2000s,true
+1691,HU,Europe,1990,2024,1990s,9,70,8,Europe / 1990s,true
+1715,RO,Europe,1990,2014,1990s,7,43,4,Europe / 1990s,true
+1759,CH,Europe,2011,2024,2010s,4,18,3,Europe / 2010s,true
+1804,JP,Asia-Pacific,1996,2014,1990s,7,49,0,Asia-Pacific / 1990s,true
+1808,CH,Europe,1971,2024,pre-1990,19,95,1,Europe / pre-1990,true
+1824,NZ,Asia-Pacific,1972,2019,pre-1990,26,114,0,Asia-Pacific / pre-1990,true
+2159,GE,Europe,1999,1999,1990s,3,7,0,Europe / 1990s,true
+2168,GE,Europe,1995,2003,1990s,3,21,0,Europe / 1990s,true
+2203,RS,Europe,2000,2000,2000s,6,7,0,Europe / 2000s,true
+2211,UA,Europe,1998,2006,1990s,5,21,0,Europe / 1990s,true
+2228,UA,Europe,2002,2012,2000s,3,28,0,Europe / 2000s,true
+2235,RU,Europe,1993,1993,1990s,3,7,0,Europe / 1990s,true
+2256,RU,Europe,2003,2019,2000s,3,30,0,Europe / 2000s,true
+2307,KR,Asia-Pacific,2000,2012,2000s,5,28,0,Asia-Pacific / 2000s,true
+3162,ME,Europe,1998,2019,1990s,8,51,0,Europe / 1990s,true
+3171,BA,Europe,2010,2019,2010s,3,23,0,Europe / 2010s,true
+3185,ME,Europe,1998,2016,1990s,5,49,0,Europe / 1990s,true
+3916,CO,Latin America,2020,2020,2020s,4,2,1,Latin America / 2020s,true
+3955,IL,Other,2019,2019,2010s,4,7,0,Other / 2010s,true
+5453,AU,Asia-Pacific,2019,2019,2010s,3,2,0,Asia-Pacific / 2010s,true

validation/outputs/blocked_fit_diagnostics/blocked_validation_manifest.csv

@@ -0,0 +1,15 @@
+field,value
+created_at,2026-08-12T17:24:30.781
+seed,20260812
+holdout_share,0.2
+eligible_parties,388
+blocked_parties,82
+text_rows_train,38202
+expert_rows_train,21162
+expert_rows_test,3916
+lr_rows_train,1897
+lr_rows_test,310
+text_sha256,8ba73dc9d378037333cc4ef1629d59350237f911b4fcd3dfd319cefb4769e23a
+expert_full_sha256,30b53244055c18ae480197cfde4cdd86f30e0c9bd5af60d371caa5647b2a362a
+lr_full_sha256,66912cc39d63b4bf63506220268e2eee2d5d94fc9c8b861eb0e25d7e79f492d2
+union_mapping_sha256,3861d65e279458e339a220df6040c98f0f5237476065d01c855e38ad2164fe5c

validation/outputs/blocked_fit_diagnostics/blocked_validation_strata.csv

@@ -0,0 +1,23 @@
+stratum,eligible_parties,blocked_parties
+Asia-Pacific / pre-1990,12,2
+Europe / 1990s,121,24
+Europe / pre-1990,103,21
+Europe / 2010s,34,7
+Europe / 2000s,47,9
+North America / pre-1990,6,1
+Latin America / pre-1990,10,2
+Latin America / 1990s,2,1
+Latin America / 2000s,7,1
+Other / 2000s,6,1
+Asia-Pacific / 2010s,4,1
+Other / 2020s,2,1
+Other / 1990s,9,2
+Asia-Pacific / 1990s,5,1
+Other / pre-1990,3,1
+Europe / 2020s,3,1
+North America / 2000s,2,1
+Asia-Pacific / 2000s,4,1
+Other / 2010s,5,1
+Latin America / 2010s,1,1
+North America / 1990s,1,1
+Latin America / 2020s,1,1

validation/outputs/blocked_fit_diagnostics/input_sha256sums.txt

@@ -0,0 +1,5 @@
+8ba73dc9d378037333cc4ef1629d59350237f911b4fcd3dfd319cefb4769e23a  _local/revision/blocked_party/text_data.csv
+a1b5031df7754ba33234e8e2ec40bb0f0d10b0aed12b277a8ea1f7876fb0f658  _local/revision/blocked_party/expert.csv
+8129e85cd13877c7a5c8698430be279265d1bb9b9dd16438f55b7159597b5af7  _local/revision/blocked_party/lr_data.csv
+ed05b52f16139ebce88d3d281e19f4b8140fa1b0f0791fa8fb00ac2ccf347c1f  _local/revision/blocked_party/expert_test.csv
+fcf131031e9bac5231fdd829b4d2f0b6ac8d9a2de748175a5fb897be048a7abe  _local/revision/blocked_party/lr_data_test.csv

validation/outputs/blocked_fit_diagnostics/metadata.json

@@ -0,0 +1,39 @@
+{
+  "year0": 1943,
+  "mean_ess": 3900.832212330333,
+  "num_samples": 1000,
+  "max_depth": 15,
+  "num_chains": 4,
+  "run_id": "run_2026-08-13_00-02-50",
+  "files": {
+    "chain_size_gb": 2.28,
+    "data": [
+      "expert_dim.csv",
+      "expert_lr.csv",
+      "segment_info.csv",
+      "segment_year_map.csv",
+      "stan_data.json",
+      "text_data.csv"
+    ],
+    "total_size_gb": 9.12,
+    "chains": [
+      "chains/chain_1.csv",
+      "chains/chain_2.csv",
+      "chains/chain_3.csv",
+      "chains/chain_4.csv"
+    ]
+  },
+  "max_rhat": 1.02529,
+  "model_file": "models/stan_model_2dim_v6.stan",
+  "dimensions": [
+    "economic_lr",
+    "galtan"
+  ],
+  "convergence_status": "excellent",
+  "mean_rhat": 1.001168748932864,
+  "model_version": "2dim",
+  "min_ess": 136.47,
+  "num_warmup": 1000,
+  "timestamp": "2026-08-13_00-02-50",
+  "adapt_delta": 0.95
+}

validation/outputs/blocked_fit_diagnostics/model_fit.environment

@@ -0,0 +1,6 @@
+CMDSTAN_HOME=/opt/agent-tools/cmdstan-2.39.0
+JULIA_NUM_THREADS=4
+STAN_NUM_THREADS=1
+PARTY2D_NUM_CHAINS=4
+PARTY2D_NUM_WARMUP=1000
+PARTY2D_NUM_SAMPLES=1000

validation/outputs/blocked_fit_diagnostics/stanc_version.txt

@@ -0,0 +1 @@
+stanc3 v2.39.0 (Unix)

validation/outputs/blocked_validation_summary.csv

@@ -0,0 +1,48 @@
+dimension,group_type,group,n,parties,pearson_r,mae,rmse,bias_expert_minus_model,latent_interval_overlap_95,latent_interval_overlap_ci_lower,latent_interval_overlap_ci_upper,mean_interval_width,mean_nearest_text_distance
+economic_lr,overall,All matched held-out ratings,1095,76,0.5475349609862943,0.15659217513079526,0.20393539783060147,-0.05532600480562651,0.5168949771689497,0.4872890521846218,0.5463827709425695,0.26430020129022835,0.16712328767123288
+economic_lr,decade,1970s,118,23,0.6073768641760265,0.14287929777127256,0.19854739676618133,-0.10205433290640958,0.6101694915254238,0.5200255656951949,0.6933662479382441,0.25169848501186437,0.0
+economic_lr,decade,2010s,354,57,0.5992779154544512,0.1501300890725984,0.1885321056791222,0.021169532897549623,0.5254237288135594,0.47341101610110464,0.57689056985201,0.26575748477902544,0.2740112994350282
+economic_lr,decade,2000s,295,55,0.5565144172854626,0.15858892901808844,0.20126848038834502,-0.05233537589313869,0.4847457627118644,0.4282780233542677,0.5416056876150218,0.26056229448652546,0.17288135593220338
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+economic_lr,decade,1980s,108,20,0.4965786440080896,0.17854227265283956,0.23865365933045032,-0.1349662304081571,0.4722222222222222,0.38064769707967805,0.56570500173722,0.24913035199722225,0.0
+economic_lr,decade,2020s,12,7,0.7503511986609598,0.11774978066574382,0.1393618688955034,0.0122078457365813,0.75,0.46768966087934005,0.9110599603710386,0.4404074548520834,0.25
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+economic_lr,region,Europe,893,59,0.5432249309980023,0.1511392922038456,0.20048220769183805,-0.04801670847392163,0.5397536394176932,0.5069623693775478,0.5722043418900828,0.2582198076309352,0.17245240761478164
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+economic_lr,text_distance_class,direct text,971,74,0.5470435275586387,0.15948847158646431,0.20840482347744635,-0.06628643977796411,0.505664263645726,0.47425628054953844,0.537027603929745,0.2590549584609939,0.0
+economic_lr,text_distance_class,nearby text (1--3 years),124,41,0.6274910136969545,0.1339123053045472,0.16479794566578806,0.030501272276146137,0.6048387096774194,0.5168822918241138,0.686494386815703,0.3053738366707661,1.4758064516129032
+economic_lr,project,V-Party,914,70,0.5307923250620764,0.1655176264187938,0.21441603701361558,-0.07700198607575016,0.4890590809628009,0.45676497600150245,0.5214447717371051,0.2606530705033918,0.0437636761487965
+economic_lr,project,GPS,26,26,0.6972897756799338,0.13173814981538515,0.16398882896345623,0.04775018790861701,0.5,0.32060306315002074,0.6793969368499793,0.28959384095,0.8076923076923077
+economic_lr,project,CHES,129,31,0.8041829585092515,0.10700941979056187,0.13157526424371663,0.044641159665681336,0.6821705426356589,0.5975442490042815,0.7562605832169504,0.2815578695228682,0.7829457364341085
+economic_lr,project,POPPA,26,21,0.8377220415965767,0.11368900666387213,0.15035280874886425,0.10760098186837214,0.6923076923076923,0.5001138848576876,0.8349887906020732,0.2815926515211539,0.8076923076923077
+economic_lr,item,lrecon_vparty,457,70,0.6526133931659629,0.12080138892236934,0.1567424568814474,-0.02190935296123517,0.6148796498905909,0.5694819726873812,0.6583620413946809,0.2606530705033917,0.0437636761487965
+economic_lr,item,welf_vparty,457,70,0.4769148611627237,0.21023386391521848,0.2595771100617617,-0.13209461919026505,0.36323851203501095,0.32045365106651325,0.40830347502626996,0.2606530705033917,0.0437636761487965
+economic_lr,item,lrecon_gps,26,26,0.6972897756799338,0.13173814981538515,0.16398882896345623,0.04775018790861701,0.5,0.32060306315002074,0.6793969368499793,0.28959384095,0.8076923076923077
+economic_lr,item,lrecon_ches,129,31,0.8041829585092515,0.10700941979056187,0.13157526424371663,0.044641159665681336,0.6821705426356589,0.5975442490042815,0.7562605832169504,0.2815578695228682,0.7829457364341085
+economic_lr,item,lrecon_poppa,26,21,0.8377220415965767,0.11368900666387213,0.15035280874886425,0.10760098186837214,0.6923076923076923,0.5001138848576876,0.8349887906020732,0.2815926515211539,0.8076923076923077
+galtan,overall,All matched held-out ratings,2428,76,0.3879064541660041,0.18914006264912334,0.23816373704876254,-0.013710351768187365,0.4126853377265239,0.39325537210953215,0.4323911667110884,0.258665849890383,0.0914332784184514
+galtan,decade,1970s,287,23,0.2402564326635899,0.21665443860032066,0.25724972613838026,0.0744907055227787,0.3344947735191638,0.2824119799338199,0.3909497399852015,0.26474203940810126,0.0
+galtan,decade,2010s,693,57,0.4606647139522965,0.1935411541181932,0.24590414523794452,-0.050660741407812654,0.3838383838383838,0.3483644977339694,0.4205930386702865,0.24824147265028856,0.12265512265512266
+galtan,decade,2000s,676,55,0.3870265819854018,0.18629380051270433,0.23690167706252432,-0.0468645901306479,0.4275147928994083,0.39073352449867466,0.4651152496863708,0.258417938360392,0.11538461538461539
+galtan,decade,1990s,499,50,0.41212741006828396,0.17126387793509984,0.2212178226996366,-0.019602997434269034,0.48897795591182364,0.4453697785277533,0.5327545453149837,0.26606359520380773,0.11823647294589178
+galtan,decade,1980s,266,20,0.3476988123327735,0.18921982830486436,0.2308346130602219,0.08154082475673356,0.37969924812030076,0.3234807111340447,0.4393431084697526,0.2599239549642858,0.0
+galtan,decade,2020s,7,5,0.6324675234649824,0.17149569493926073,0.20386312323528286,0.030403782877832106,0.8571428571428571,0.48686549668097007,0.9743210440510253,0.49033504546428575,0.0
+galtan,region,Asia-Pacific,142,5,-0.06990999699587813,0.2028535844559974,0.2421448847066388,-0.04764709776122232,0.43661971830985913,0.3577770958258817,0.5188013289856945,0.31393695714260567,0.007042253521126761
+galtan,region,Europe,1938,59,0.4006536844413476,0.18821986618869885,0.2391185533726696,-0.007048457123688323,0.4107327141382869,0.3890267037664939,0.4327919244882515,0.24984948892156866,0.07791537667698659
+galtan,region,North America,117,3,0.15158098770839884,0.2020264962636105,0.24484307272242728,0.04760720430768876,0.2905982905982906,0.21602760266916315,0.37848289702630594,0.23609932028311967,0.0
+galtan,region,Latin America,110,4,0.510877747351673,0.15390455215418858,0.19797883572628092,-0.07917475272712499,0.6272727272727273,0.5340503169531756,0.7119054672241376,0.3662500658409092,0.6363636363636364
+galtan,region,Other,121,5,0.3370413394210674,0.20735670781668103,0.24493355112293227,-0.08036162321796078,0.33884297520661155,0.2606254540535898,0.4269786779024248,0.25902643284276866,0.0
+galtan,text_distance_class,direct text,2293,74,0.3817373914639877,0.1904499145514716,0.23993647489228756,-0.014524078018235874,0.404709986916703,0.38479506099767413,0.42494366891735347,0.25436052910535323,0.0
+galtan,text_distance_class,nearby text (1--3 years),135,40,0.4399561874640988,0.16689198552257195,0.20573340547684066,0.00011093927893245328,0.5481481481481482,0.46402181812792126,0.6296100616545071,0.3317925207057409,1.6444444444444444
+galtan,project,V-Party,2273,70,0.3740693791970041,0.1911074338064693,0.24060334153635682,-0.018396438536216624,0.4047514298284206,0.3847495239974894,0.4250747518765987,0.2566477345279916,0.04399472063352398
+galtan,project,GPS,26,26,0.5443693144157825,0.17671657291477977,0.21780593561007713,0.012007051328791764,0.46153846153846156,0.28755582695234905,0.6454236379556988,0.2777555471798077,0.8076923076923077
+galtan,project,CHES,129,31,0.5869373239223193,0.15697863700916778,0.19496790336712624,0.06367587104738649,0.5426356589147286,0.456675986048851,0.6261294002156924,0.2903778195740311,0.7829457364341085
+galtan,item,culsup_vparty,457,70,0.5366188718286774,0.16311644869435024,0.21359963974416982,0.011168223905568687,0.4638949671772429,0.418663126356892,0.5097287549316027,0.25691081806515326,0.0437636761487965
+galtan,item,gender_vparty,445,70,0.32459509946925547,0.2164337137525867,0.2608755427368426,0.12306040878157333,0.34606741573033706,0.3033546905138159,0.39141513474302303,0.25556702282926974,0.0449438202247191
+galtan,item,immig_vparty,457,70,0.5004863614572931,0.13311846254318913,0.16732357853899973,-0.011445561652418225,0.5776805251641138,0.5319316389137728,0.6221343134655263,0.25691081806515326,0.0437636761487965
+galtan,item,lgbt_vparty,457,70,0.46354086621523605,0.15471230984590562,0.18981764808744647,0.04310285191432138,0.474835886214442,0.42945203114163966,0.5206392800594324,0.25691081806515326,0.0437636761487965
+galtan,item,relig_vparty,457,70,0.4185372751933906,0.288821256864484,0.33467597534992094,-0.25415371263710096,0.15973741794310722,0.12900420700742332,0.19614352271142144,0.25691081806515326,0.0437636761487965
+galtan,item,libcon_gps,26,26,0.5443693144157825,0.17671657291477977,0.21780593561007713,0.012007051328791764,0.46153846153846156,0.28755582695234905,0.6454236379556988,0.2777555471798077,0.8076923076923077
+galtan,item,galtan_ches,129,31,0.5869373239223193,0.15697863700916778,0.19496790336712624,0.06367587104738649,0.5426356589147286,0.456675986048851,0.6261294002156924,0.2903778195740311,0.7829457364341085

validation/outputs/party_landmark_plot_data.csv

@@ -0,0 +1,18 @@
+"party_id","target_year","label","status","actual_year","party_name","country","economic_lr","economic_lower","economic_upper","galtan","galtan_lower","galtan_upper","source_support_class","era"
+383,1972,"SPD 1972","exact",1972,"Social Democratic Party of Germany","DE",0.271573270375,0.162936225,0.38761415,0.370199619125,0.2680677,0.473299475,"both_direct_or_nearby","Historical & Cold War Era (1970–1999)"
+383,2021,"SPD 2021","exact",2021,"Social Democratic Party of Germany","DE",0.2363366825,0.148430575,0.330595375,0.36824540875,0.27656695,0.461600275,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+1375,1983,"CDU 1983","exact",1983,"Christian Democratic Union","DE",0.704186118875,0.559885375,0.832835125,0.6167079735,0.5088347,0.716463575,"text_only_direct_or_nearby","Historical & Cold War Era (1970–1999)"
+1375,2021,"CDU 2021","exact",2021,"Christian Democratic Union","DE",0.57714137175,0.45681705,0.69505505,0.536185121875,0.433751725,0.636189175,"text_only_direct_or_nearby","Contemporary Era (2000–2022)"
+1516,1983,"Labour 1983","exact",1983,"Labour Party","GB",0.259080600525,0.1453436,0.3814767,0.34731692875,0.243851525,0.45443645,"both_direct_or_nearby","Historical & Cold War Era (1970–1999)"
+1516,1997,"Labour 1997","exact",1997,"Labour Party","GB",0.540093913375,0.45312245,0.63004375,0.50597976425,0.44681665,0.566031675,"both_direct_or_nearby","Historical & Cold War Era (1970–1999)"
+1567,1979,"Conservatives 1979","exact",1979,"Conservative Party","GB",0.863733304625,0.78370345,0.932120225,0.58421918175,0.47261405,0.691922375,"both_direct_or_nearby","Historical & Cold War Era (1970–1999)"
+1567,2019,"Conservatives 2019","exact",2019,"Conservative Party","GB",0.734806347125,0.6674518,0.805338225,0.604817389125,0.5601045,0.6518521,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+487,1994,"Swedish SAP 1994","exact",1994,"Social Democratic Labour Party","SE",0.58329747225,0.45880465,0.706358175,0.323307833,0.22678185,0.42106565,"both_direct_or_nearby","Historical & Cold War Era (1970–1999)"
+487,2022,"Swedish SAP 2022","exact",2022,"Social Democratic Labour Party","SE",0.271161611625,0.18001335,0.364595125,0.431600183875,0.333020475,0.530597425,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+409,2010,"Sweden Democrats 2010","exact",2010,"Sweden Democrats","SE",0.57402493025,0.47088145,0.681297275,0.721373298875,0.653960175,0.78768305,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+409,2022,"Sweden Democrats 2022","exact",2022,"Sweden Democrats","SE",0.609638368125,0.490017925,0.7238046,0.753425659,0.653108875,0.84672335,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+433,1988,"French FN 1988","exact",1988,"National Front","FR",0.78687783275,0.6924592,0.8758855,0.797982186375,0.7315981,0.86105335,"both_direct_or_nearby","Historical & Cold War Era (1970–1999)"
+433,2022,"French FN 2022","exact",2022,"National Front","FR",0.524320955125,0.398589175,0.645052275,0.829604491,0.73907015,0.911430725,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+1545,2021,"The Left 2021","exact",2021,"The Left","DE",0.05463340125125,0.0205853625,0.104672875,0.2266517688,0.13204575,0.336095325,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+432,2020,"US Democrats 2020","exact",2020,"Democratic Party","US",0.299892256625,0.1963003,0.410606025,0.3345769065,0.263694925,0.405684575,"both_direct_or_nearby","Contemporary Era (2000–2022)"
+809,2020,"US Republicans 2020","exact",2020,"Republican Party","US",0.8507495345,0.77742365,0.917357075,0.721311676375,0.65728435,0.78442835,"both_direct_or_nearby","Contemporary Era (2000–2022)"

validation/outputs/party_trajectory_plot_data.csv

@@ -0,0 +1,147 @@
+"party_id","party_label","country","year","source_support_class","dimension","estimate","lower","upper"
+1567,"United Kingdom: Conservatives","GB",1945,"text_only_direct_or_nearby","Economic",0.7897200205,0.68672145,0.882517725
+1567,"United Kingdom: Conservatives","GB",1950,"text_only_direct_or_nearby","Economic",0.696324671375,0.58645,0.7999215
+1567,"United Kingdom: Conservatives","GB",1951,"text_only_direct_or_nearby","Economic",0.648801438125,0.509441425,0.7756712
+1567,"United Kingdom: Conservatives","GB",1955,"text_only_direct_or_nearby","Economic",0.559596760875,0.4111588,0.696031575
+1567,"United Kingdom: Conservatives","GB",1959,"text_only_direct_or_nearby","Economic",0.56006642225,0.388645375,0.718084975
+1567,"United Kingdom: Conservatives","GB",1964,"text_only_direct_or_nearby","Economic",0.713843190625,0.5883803,0.829548525
+1567,"United Kingdom: Conservatives","GB",1966,"text_only_direct_or_nearby","Economic",0.79834168225,0.6939884,0.890500425
+1567,"United Kingdom: Conservatives","GB",1970,"both_direct_or_nearby","Economic",0.67897010575,0.55999,0.79320825
+1567,"United Kingdom: Conservatives","GB",1974,"both_direct_or_nearby","Economic",0.68530368975,0.6022573,0.771763125
+1567,"United Kingdom: Conservatives","GB",1979,"both_direct_or_nearby","Economic",0.863733304625,0.78370345,0.932120225
+1567,"United Kingdom: Conservatives","GB",1983,"both_direct_or_nearby","Economic",0.897143095,0.823332125,0.953360675
+1567,"United Kingdom: Conservatives","GB",1987,"both_direct_or_nearby","Economic",0.864987285375,0.782492075,0.9328753
+1567,"United Kingdom: Conservatives","GB",1992,"both_direct_or_nearby","Economic",0.80683245025,0.731234925,0.88153825
+1567,"United Kingdom: Conservatives","GB",1997,"both_direct_or_nearby","Economic",0.815790791,0.741322225,0.886000175
+1567,"United Kingdom: Conservatives","GB",2001,"both_direct_or_nearby","Economic",0.735943766375,0.64358755,0.825612475
+1567,"United Kingdom: Conservatives","GB",2005,"both_direct_or_nearby","Economic",0.7468415025,0.657667875,0.8347423
+1567,"United Kingdom: Conservatives","GB",2010,"both_direct_or_nearby","Economic",0.807639387875,0.737347625,0.877409025
+1567,"United Kingdom: Conservatives","GB",2015,"both_direct_or_nearby","Economic",0.650294524875,0.582851025,0.72313155
+1567,"United Kingdom: Conservatives","GB",2017,"both_direct_or_nearby","Economic",0.680683625125,0.607886575,0.758350075
+1567,"United Kingdom: Conservatives","GB",2019,"both_direct_or_nearby","Economic",0.734806347125,0.6674518,0.805338225
+1567,"United Kingdom: Conservatives","GB",2024,"both_direct_or_nearby","Economic",0.696441988625,0.611536275,0.7845477
+383,"Germany: SPD","DE",1949,"text_only_direct_or_nearby","Economic",0.2594422284125,0.124083075,0.416025325
+383,"Germany: SPD","DE",1953,"text_only_direct_or_nearby","Economic",0.3044620151625,0.172417475,0.44553015
+383,"Germany: SPD","DE",1957,"text_only_direct_or_nearby","Economic",0.364714905375,0.217271,0.518439625
+383,"Germany: SPD","DE",1961,"text_only_direct_or_nearby","Economic",0.45071135275,0.3107251,0.58867
+383,"Germany: SPD","DE",1965,"text_only_direct_or_nearby","Economic",0.40004889475,0.253314775,0.5547562
+383,"Germany: SPD","DE",1969,"text_only_direct_or_nearby","Economic",0.36430581075,0.234999825,0.496653
+383,"Germany: SPD","DE",1972,"both_direct_or_nearby","Economic",0.271573270375,0.162936225,0.38761415
+383,"Germany: SPD","DE",1976,"both_direct_or_nearby","Economic",0.22760841445,0.1408578,0.317522325
+383,"Germany: SPD","DE",1980,"both_direct_or_nearby","Economic",0.2854944402625,0.176353725,0.402044875
+383,"Germany: SPD","DE",1983,"both_direct_or_nearby","Economic",0.3201057485,0.198932275,0.449359525
+383,"Germany: SPD","DE",1987,"both_direct_or_nearby","Economic",0.3082503073875,0.1890146,0.43586885
+383,"Germany: SPD","DE",1990,"both_direct_or_nearby","Economic",0.2997481731625,0.1826493,0.42186305
+383,"Germany: SPD","DE",1994,"both_direct_or_nearby","Economic",0.344258320125,0.247450525,0.440361825
+383,"Germany: SPD","DE",1998,"both_direct_or_nearby","Economic",0.42037938775,0.335025575,0.504438225
+383,"Germany: SPD","DE",2002,"both_direct_or_nearby","Economic",0.354348980875,0.285402475,0.419218875
+383,"Germany: SPD","DE",2005,"both_direct_or_nearby","Economic",0.408510935875,0.326714325,0.4912897
+383,"Germany: SPD","DE",2009,"both_direct_or_nearby","Economic",0.2258038923125,0.154613,0.2986832
+383,"Germany: SPD","DE",2013,"both_direct_or_nearby","Economic",0.2547526295,0.177729475,0.3305311
+383,"Germany: SPD","DE",2017,"both_direct_or_nearby","Economic",0.2187940564125,0.150431,0.288139
+383,"Germany: SPD","DE",2021,"both_direct_or_nearby","Economic",0.2363366825,0.148430575,0.330595375
+383,"Germany: SPD","DE",2025,"both_direct_or_nearby","Economic",0.24988987125,0.155485225,0.353246025
+379,"Denmark: Social Democrats","DK",1945,"text_only_direct_or_nearby","Economic",0.39289630125,0.306313775,0.4744366
+379,"Denmark: Social Democrats","DK",1947,"text_only_direct_or_nearby","Economic",0.4133295765,0.326647125,0.497578325
+379,"Denmark: Social Democrats","DK",1950,"text_only_direct_or_nearby","Economic",0.402314539625,0.313989,0.48990055
+379,"Denmark: Social Democrats","DK",1953,"text_only_direct_or_nearby","Economic",0.404059824625,0.31188405,0.492347325
+379,"Denmark: Social Democrats","DK",1957,"text_only_direct_or_nearby","Economic",0.386770333875,0.2915608,0.4740622
+379,"Denmark: Social Democrats","DK",1960,"text_only_direct_or_nearby","Economic",0.39128767025,0.30583805,0.46915175
+379,"Denmark: Social Democrats","DK",1964,"text_only_direct_or_nearby","Economic",0.41323511225,0.3319368,0.490628525
+379,"Denmark: Social Democrats","DK",1966,"text_only_direct_or_nearby","Economic",0.424408032125,0.348613025,0.502216325
+379,"Denmark: Social Democrats","DK",1968,"text_only_direct_or_nearby","Economic",0.426020581125,0.346608825,0.502008425
+379,"Denmark: Social Democrats","DK",1971,"both_direct_or_nearby","Economic",0.3838693815,0.31048555,0.4534922
+379,"Denmark: Social Democrats","DK",1973,"both_direct_or_nearby","Economic",0.36916859375,0.292048475,0.4383149
+379,"Denmark: Social Democrats","DK",1975,"both_direct_or_nearby","Economic",0.2551262250375,0.1655264,0.348229075
+379,"Denmark: Social Democrats","DK",1977,"both_direct_or_nearby","Economic",0.2030203874375,0.11497425,0.301540125
+379,"Denmark: Social Democrats","DK",1979,"both_direct_or_nearby","Economic",0.1862332029375,0.10144465,0.2859892
+379,"Denmark: Social Democrats","DK",1981,"both_direct_or_nearby","Economic",0.1919036092625,0.1020977,0.2975764
+379,"Denmark: Social Democrats","DK",1984,"both_direct_or_nearby","Economic",0.1734391782625,0.08982039,0.2725962
+379,"Denmark: Social Democrats","DK",1987,"both_direct_or_nearby","Economic",0.1872400464625,0.10188485,0.281978775
+379,"Denmark: Social Democrats","DK",1988,"both_direct_or_nearby","Economic",0.1844144026,0.10014465,0.278666375
+379,"Denmark: Social Democrats","DK",1990,"both_direct_or_nearby","Economic",0.1593657011125,0.0820198525,0.250674325
+379,"Denmark: Social Democrats","DK",1994,"both_direct_or_nearby","Economic",0.18027195025,0.0911998025,0.286319075
+379,"Denmark: Social Democrats","DK",1998,"both_direct_or_nearby","Economic",0.2332968959625,0.14133775,0.3324363
+379,"Denmark: Social Democrats","DK",2001,"both_direct_or_nearby","Economic",0.25665459875,0.167550175,0.3497182
+379,"Denmark: Social Democrats","DK",2005,"both_direct_or_nearby","Economic",0.243111738,0.15668705,0.33332095
+379,"Denmark: Social Democrats","DK",2007,"both_direct_or_nearby","Economic",0.2504832553,0.1616851,0.347202325
+379,"Denmark: Social Democrats","DK",2011,"both_direct_or_nearby","Economic",0.277551306,0.187687975,0.3698644
+379,"Denmark: Social Democrats","DK",2015,"both_direct_or_nearby","Economic",0.2492670365,0.160125675,0.34358355
+379,"Denmark: Social Democrats","DK",2019,"both_direct_or_nearby","Economic",0.254542113875,0.180740975,0.32727345
+409,"Sweden: Sweden Democrats","SE",2010,"both_direct_or_nearby","Economic",0.57402493025,0.47088145,0.681297275
+409,"Sweden: Sweden Democrats","SE",2014,"both_direct_or_nearby","Economic",0.514751535125,0.4326713,0.602047575
+409,"Sweden: Sweden Democrats","SE",2018,"both_direct_or_nearby","Economic",0.499110805875,0.397517425,0.59893585
+409,"Sweden: Sweden Democrats","SE",2022,"both_direct_or_nearby","Economic",0.609638368125,0.490017925,0.7238046
+1567,"United Kingdom: Conservatives","GB",1945,"text_only_direct_or_nearby","Cultural",0.42283955775,0.30617415,0.539308425
+1567,"United Kingdom: Conservatives","GB",1950,"text_only_direct_or_nearby","Cultural",0.597652829625,0.493804425,0.695553875
+1567,"United Kingdom: Conservatives","GB",1951,"text_only_direct_or_nearby","Cultural",0.5963407815,0.4953524,0.694196075
+1567,"United Kingdom: Conservatives","GB",1955,"text_only_direct_or_nearby","Cultural",0.4481433145,0.349619975,0.546246075
+1567,"United Kingdom: Conservatives","GB",1959,"text_only_direct_or_nearby","Cultural",0.379823589625,0.24070795,0.5271287
+1567,"United Kingdom: Conservatives","GB",1964,"text_only_direct_or_nearby","Cultural",0.4359372675,0.309309225,0.5597016
+1567,"United Kingdom: Conservatives","GB",1966,"text_only_direct_or_nearby","Cultural",0.433826746,0.305561125,0.564284875
+1567,"United Kingdom: Conservatives","GB",1970,"both_direct_or_nearby","Cultural",0.586682156125,0.487144225,0.681696075
+1567,"United Kingdom: Conservatives","GB",1974,"both_direct_or_nearby","Cultural",0.582726903625,0.512885,0.65400205
+1567,"United Kingdom: Conservatives","GB",1979,"both_direct_or_nearby","Cultural",0.58421918175,0.47261405,0.691922375
+1567,"United Kingdom: Conservatives","GB",1983,"both_direct_or_nearby","Cultural",0.54862467925,0.4436958,0.651204175
+1567,"United Kingdom: Conservatives","GB",1987,"both_direct_or_nearby","Cultural",0.5044723465,0.398577575,0.60606875
+1567,"United Kingdom: Conservatives","GB",1992,"both_direct_or_nearby","Cultural",0.608807442,0.53829935,0.67952055
+1567,"United Kingdom: Conservatives","GB",1997,"both_direct_or_nearby","Cultural",0.653249609625,0.5903779,0.719252725
+1567,"United Kingdom: Conservatives","GB",2001,"both_direct_or_nearby","Cultural",0.62022832225,0.5519112,0.68966615
+1567,"United Kingdom: Conservatives","GB",2005,"both_direct_or_nearby","Cultural",0.63051460775,0.562743875,0.6984813
+1567,"United Kingdom: Conservatives","GB",2010,"both_direct_or_nearby","Cultural",0.515056946125,0.464064225,0.5684148
+1567,"United Kingdom: Conservatives","GB",2015,"both_direct_or_nearby","Cultural",0.53108352025,0.47741145,0.585770875
+1567,"United Kingdom: Conservatives","GB",2017,"both_direct_or_nearby","Cultural",0.550617655875,0.503622825,0.597269
+1567,"United Kingdom: Conservatives","GB",2019,"both_direct_or_nearby","Cultural",0.604817389125,0.5601045,0.6518521
+1567,"United Kingdom: Conservatives","GB",2024,"both_direct_or_nearby","Cultural",0.64147235475,0.568830525,0.717038025
+383,"Germany: SPD","DE",1949,"text_only_direct_or_nearby","Cultural",0.4562653665,0.29105445,0.6148909
+383,"Germany: SPD","DE",1953,"text_only_direct_or_nearby","Cultural",0.41201424225,0.239960775,0.59720855
+383,"Germany: SPD","DE",1957,"text_only_direct_or_nearby","Cultural",0.361933815225,0.2020638,0.530546125
+383,"Germany: SPD","DE",1961,"text_only_direct_or_nearby","Cultural",0.3403875836625,0.189634975,0.502483725
+383,"Germany: SPD","DE",1965,"text_only_direct_or_nearby","Cultural",0.349500525125,0.197610625,0.519782425
+383,"Germany: SPD","DE",1969,"text_only_direct_or_nearby","Cultural",0.367453271375,0.2312429,0.51644905
+383,"Germany: SPD","DE",1972,"both_direct_or_nearby","Cultural",0.370199619125,0.2680677,0.473299475
+383,"Germany: SPD","DE",1976,"both_direct_or_nearby","Cultural",0.28787436475,0.21332925,0.364130925
+383,"Germany: SPD","DE",1980,"both_direct_or_nearby","Cultural",0.346500680875,0.245533525,0.448428875
+383,"Germany: SPD","DE",1983,"both_direct_or_nearby","Cultural",0.3643932085,0.2585273,0.474535775
+383,"Germany: SPD","DE",1987,"both_direct_or_nearby","Cultural",0.373242374375,0.266124675,0.479610175
+383,"Germany: SPD","DE",1990,"both_direct_or_nearby","Cultural",0.354967389625,0.25200245,0.460687325
+383,"Germany: SPD","DE",1994,"both_direct_or_nearby","Cultural",0.410000861625,0.3313879,0.488224525
+383,"Germany: SPD","DE",1998,"both_direct_or_nearby","Cultural",0.458140040875,0.389836225,0.525399375
+383,"Germany: SPD","DE",2002,"both_direct_or_nearby","Cultural",0.442940016,0.3876151,0.4964075
+383,"Germany: SPD","DE",2005,"both_direct_or_nearby","Cultural",0.456470195875,0.39176415,0.5189629
+383,"Germany: SPD","DE",2009,"both_direct_or_nearby","Cultural",0.389663805625,0.32557465,0.451253625
+383,"Germany: SPD","DE",2013,"both_direct_or_nearby","Cultural",0.2109102855,0.15503785,0.268407625
+383,"Germany: SPD","DE",2017,"both_direct_or_nearby","Cultural",0.40809383225,0.35346185,0.45866175
+383,"Germany: SPD","DE",2021,"both_direct_or_nearby","Cultural",0.36824540875,0.27656695,0.461600275
+383,"Germany: SPD","DE",2025,"both_direct_or_nearby","Cultural",0.3699090585,0.27155195,0.46999925
+379,"Denmark: Social Democrats","DK",1945,"text_only_direct_or_nearby","Cultural",0.367253073125,0.222935775,0.513292475
+379,"Denmark: Social Democrats","DK",1947,"text_only_direct_or_nearby","Cultural",0.38745918675,0.234832075,0.545806525
+379,"Denmark: Social Democrats","DK",1950,"text_only_direct_or_nearby","Cultural",0.3968814625,0.2531828,0.544707225
+379,"Denmark: Social Democrats","DK",1953,"text_only_direct_or_nearby","Cultural",0.42331649125,0.283525625,0.5604742
+379,"Denmark: Social Democrats","DK",1957,"text_only_direct_or_nearby","Cultural",0.42351804925,0.268476675,0.589941825
+379,"Denmark: Social Democrats","DK",1960,"text_only_direct_or_nearby","Cultural",0.384011194875,0.24432495,0.52920335
+379,"Denmark: Social Democrats","DK",1964,"text_only_direct_or_nearby","Cultural",0.362245514375,0.224588175,0.502448675
+379,"Denmark: Social Democrats","DK",1966,"text_only_direct_or_nearby","Cultural",0.3679157005,0.230511325,0.5031338
+379,"Denmark: Social Democrats","DK",1968,"text_only_direct_or_nearby","Cultural",0.382175081625,0.25659165,0.507958075
+379,"Denmark: Social Democrats","DK",1971,"both_direct_or_nearby","Cultural",0.470225764375,0.36279785,0.577130125
+379,"Denmark: Social Democrats","DK",1973,"both_direct_or_nearby","Cultural",0.501466689875,0.3986878,0.60298055
+379,"Denmark: Social Democrats","DK",1975,"both_direct_or_nearby","Cultural",0.51072920025,0.41364105,0.608167125
+379,"Denmark: Social Democrats","DK",1977,"both_direct_or_nearby","Cultural",0.48888551625,0.3865325,0.588461525
+379,"Denmark: Social Democrats","DK",1979,"both_direct_or_nearby","Cultural",0.469331547625,0.36461055,0.5720459
+379,"Denmark: Social Democrats","DK",1981,"both_direct_or_nearby","Cultural",0.440505821125,0.334911475,0.544872125
+379,"Denmark: Social Democrats","DK",1984,"both_direct_or_nearby","Cultural",0.427927746125,0.3199225,0.535149325
+379,"Denmark: Social Democrats","DK",1987,"both_direct_or_nearby","Cultural",0.42510295375,0.332117075,0.518948075
+379,"Denmark: Social Democrats","DK",1988,"both_direct_or_nearby","Cultural",0.4189467795,0.3290549,0.51075845
+379,"Denmark: Social Democrats","DK",1990,"both_direct_or_nearby","Cultural",0.394384283125,0.289929725,0.498693825
+379,"Denmark: Social Democrats","DK",1994,"both_direct_or_nearby","Cultural",0.365051991,0.259654975,0.469250275
+379,"Denmark: Social Democrats","DK",1998,"both_direct_or_nearby","Cultural",0.4057949125,0.318039475,0.493913625
+379,"Denmark: Social Democrats","DK",2001,"both_direct_or_nearby","Cultural",0.377267655375,0.2986888,0.456555875
+379,"Denmark: Social Democrats","DK",2005,"both_direct_or_nearby","Cultural",0.3840174395,0.296652025,0.472492425
+379,"Denmark: Social Democrats","DK",2007,"both_direct_or_nearby","Cultural",0.377073825875,0.288222975,0.46642125
+379,"Denmark: Social Democrats","DK",2011,"both_direct_or_nearby","Cultural",0.44217709925,0.351102525,0.5332076
+379,"Denmark: Social Democrats","DK",2015,"both_direct_or_nearby","Cultural",0.5274942445,0.44580275,0.608968575
+379,"Denmark: Social Democrats","DK",2019,"both_direct_or_nearby","Cultural",0.440971318625,0.38293125,0.4998911
+409,"Sweden: Sweden Democrats","SE",2010,"both_direct_or_nearby","Cultural",0.721373298875,0.653960175,0.78768305
+409,"Sweden: Sweden Democrats","SE",2014,"both_direct_or_nearby","Cultural",0.750375772125,0.690054075,0.81067825
+409,"Sweden: Sweden Democrats","SE",2018,"both_direct_or_nearby","Cultural",0.671777804625,0.5936524,0.747156725
+409,"Sweden: Sweden Democrats","SE",2022,"both_direct_or_nearby","Cultural",0.753425659,0.653108875,0.84672335

validation/outputs/ppc/posterior_predictive_by_dimension_country.csv

@@ -0,0 +1,131 @@
+dim_idx,country,n,covered,missed,observed_coverage,coverage_ci_lower,coverage_ci_upper,mean_interval_width,share_of_all_misses
+2,DK,591,475,116,0.8037225042301185,0.7697822014838583,0.8337398117714353,0.34096506583821323,0.037108125399872044
+2,SE,502,397,105,0.7908366533864541,0.753115778828177,0.8241400261510892,0.32724096117398543,0.03358925143953935
+2,FI,441,362,79,0.8208616780045351,0.7823477062456757,0.8538338019585928,0.3521254779175972,0.02527191298784389
+2,MX,295,219,76,0.7423728813559322,0.6895752975947299,0.7889390725598938,0.23095838836601693,0.02431222008957134
+2,NO,361,287,74,0.7950138504155124,0.7503670542041302,0.8334479493480963,0.33480773282277126,0.023672424824056303
+2,IL,363,294,69,0.8099173553719008,0.7663888096836808,0.8469549332287913,0.4807755515181515,0.022072936660268713
+2,CO,217,153,64,0.7050691244239631,0.6412522930429461,0.7617514888532184,0.29239857298387406,0.020473448496481125
+1,SE,251,189,62,0.7529880478087649,0.6960862604122187,0.8022625178092335,0.3335369874622401,0.01983365323096609
+2,ES,387,327,60,0.8449612403100775,0.8055273129985262,0.8776138879381191,0.3107361818602786,0.019193857965451054
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+1,2020,466,462,4,0.9914163090128756,0.9781395065563742,0.9966571057487091,0.2480109465949484,0.0012795905310300703

validation/outputs/ppc/posterior_predictive_by_dimension_item.csv

@@ -0,0 +1,13 @@
+dim_idx,project,var,n,covered,missed,observed_coverage,coverage_ci_lower,coverage_ci_upper,mean_interval_width,share_of_all_misses
+2,V-Party,gender_vparty,2802,2119,683,0.7562455389007852,0.7400041833684662,0.7717852205665244,0.40881512947959253,0.21849008317338453
+2,V-Party,relig_vparty,2828,2262,566,0.7998585572842999,0.7847095841432594,0.8141939703493366,0.2882881086220928,0.18106206014075496
+1,V-Party,welf_vparty,2818,2261,557,0.8023420865862314,0.787231173137499,0.8166297948204475,0.4001936033568574,0.1781829814459373
+2,V-Party,culsup_vparty,2828,2387,441,0.844059405940594,0.8302220160564773,0.856963312388447,0.39195419130327286,0.14107485604606526
+2,V-Party,immig_vparty,2828,2487,341,0.8794200848656294,0.8669005216436195,0.8909102259631675,0.39156281472441545,0.1090850927703135
+2,V-Party,lgbt_vparty,2828,2579,249,0.9119519094766619,0.9009413114843197,0.921844821751381,0.4007675443770831,0.07965451055662189
+1,V-Party,lrecon_vparty,2827,2679,148,0.9476476830562434,0.9388125348768209,0.9552678686328632,0.3937581954807973,0.04734484964811261
+2,CHES,galtan_ches,1176,1109,67,0.9430272108843537,0.9282799190767855,0.9548894826462931,0.26190336027902683,0.02143314139475368
+1,CHES,lrecon_ches,1177,1145,32,0.9728122344944775,0.9618713612264376,0.9806767332851378,0.2537544114580155,0.010236724248240563
+2,GPS,libcon_gps,249,227,22,0.9116465863453815,0.8698544742943817,0.940929827023123,0.2281137815345669,0.007037747920665387
+1,GPS,lrecon_gps,249,234,15,0.9397590361445783,0.9029977562632928,0.9631571802661391,0.23441440673165534,0.0047984644913627635
+1,POPPA,lrecon_poppa,384,379,5,0.9869791666666666,0.9698853213826314,0.9944258819539885,0.2668966115681637,0.001599488163787588

validation/outputs/ppc/posterior_predictive_by_dimension_source.csv

@@ -0,0 +1,8 @@
+dim_idx,project,n,covered,missed,observed_coverage,coverage_ci_lower,coverage_ci_upper,mean_interval_width,share_of_all_misses
+2,V-Party,14114,11834,2280,0.8384582683859997,0.8322945450978664,0.8444377961971373,0.37621761885573124,0.7293666026871402
+1,V-Party,5645,4940,705,0.87511071744907,0.8662305886687013,0.883480644161761,0.3969707693328323,0.2255278310940499
+2,CHES,1176,1109,67,0.9430272108843537,0.9282799190767855,0.9548894826462931,0.26190336027902683,0.02143314139475368
+1,CHES,1177,1145,32,0.9728122344944775,0.9618713612264376,0.9806767332851378,0.2537544114580155,0.010236724248240563
+2,GPS,249,227,22,0.9116465863453815,0.8698544742943817,0.940929827023123,0.2281137815345669,0.007037747920665387
+1,GPS,249,234,15,0.9397590361445783,0.9029977562632928,0.9631571802661391,0.23441440673165534,0.0047984644913627635
+1,POPPA,384,379,5,0.9869791666666666,0.9698853213826314,0.9944258819539885,0.2668966115681637,0.001599488163787588

validation/outputs/ppc/posterior_predictive_calibration_curve.csv

@@ -0,0 +1,9 @@
+dim_idx,nominal_level,observed_coverage,covered,n
+1,0.5,0.4886653252850436,3643,7455
+1,0.8,0.7585513078470825,5655,7455
+1,0.9,0.848692152917505,6327,7455
+1,0.95,0.8984574111334674,6698,7455
+2,0.5,0.4237080893236373,6584,15539
+2,0.8,0.6785507432910741,10544,15539
+2,0.9,0.7784928245060815,12097,15539
+2,0.95,0.8475448870583693,13170,15539

validation/outputs/ppc/posterior_predictive_residual_patterns.csv

@@ -0,0 +1,31 @@
+"dim_idx","group_type","group","n","covered","observed_coverage","mean_interval_width","mean_fitted_value","mean_expert_count"
+1,"fitted_bin","<0.10",186,173,0.93010752688172,0.201343935349587,0.0742982979906544,6.90860215053763
+2,"fitted_bin","<0.10",1136,1057,0.930457746478873,0.199591148849266,0.0563123796238274,4.14700704225352
+1,"fitted_bin","0.10--0.25",1048,951,0.907442748091603,0.314041549598746,0.184525433560757,5.61927480916031
+2,"fitted_bin","0.10--0.25",2188,1825,0.834095063985375,0.317890840699561,0.178618057950006,5.16499085923218
+1,"fitted_bin","0.25--0.50",3246,2901,0.893715341959335,0.386906127490127,0.378663668829021,6.03789279112754
+2,"fitted_bin","0.25--0.50",5968,5095,0.853719839142091,0.398286462549822,0.386935866391175,5.08176943699732
+1,"fitted_bin","0.50--0.75",2556,2275,0.890062597809077,0.375315993025779,0.611178265471442,6.72339593114241
+2,"fitted_bin","0.50--0.75",5355,4448,0.83062558356676,0.392888401688966,0.602719664402968,5.07581699346405
+1,"fitted_bin","0.75--0.90",412,393,0.953883495145631,0.285225609283457,0.795682066295318,8.87621359223301
+2,"fitted_bin","0.75--0.90",782,647,0.827365728900256,0.325761069731583,0.802604598684315,6.67519181585678
+1,"fitted_bin",">=0.90",7,5,0.714285714285714,0.16328375744906,0.916229942632867,10.1428571428571
+2,"fitted_bin",">=0.90",110,98,0.890909090909091,0.152884501861608,0.932790231810077,13.6545454545455
+1,"expert_count_bin","1",426,349,0.81924882629108,0.65481986066405,0.448917410089667,1
+2,"expert_count_bin","1",771,720,0.933852140077821,0.639913191413526,0.504472582940748,1
+1,"expert_count_bin","2--3",1983,1740,0.877458396369138,0.444902409806459,0.421601867407412,2.65708522440746
+2,"expert_count_bin","2--3",4733,4075,0.860976125079231,0.435003218496791,0.406494180618051,2.56665962391718
+1,"expert_count_bin","4--5",2194,1936,0.882406563354603,0.354391432832638,0.434501893246095,4.43208751139471
+2,"expert_count_bin","4--5",5503,4597,0.835362529529348,0.344489930932836,0.411151430784599,4.34017808468108
+1,"expert_count_bin","6--10",1564,1436,0.918158567774936,0.30087431789946,0.455534718410825,7.46227621483376
+2,"expert_count_bin","6--10",3122,2531,0.810698270339526,0.291057091853021,0.468603655803318,7.02274183215887
+1,"expert_count_bin",">10",1288,1237,0.960403726708075,0.226032177926726,0.496678494666528,15.9906832298137
+2,"expert_count_bin",">10",1410,1247,0.884397163120567,0.225590440713733,0.484677797077863,15.258865248227
+1,"interval_width_quartile","Q1",2082,1964,0.943323727185399,0.234836823506568,0.427252900199234,12.5168107588857
+2,"interval_width_quartile","Q1",3667,3137,0.855467684755931,0.224611599610499,0.336836104500498,9.18571038996455
+1,"interval_width_quartile","Q2",1752,1598,0.912100456621005,0.32623893175719,0.439056079092374,5.82020547945205
+2,"interval_width_quartile","Q2",3996,3316,0.82982982982983,0.325761369694064,0.451615985560038,5.59934934934935
+1,"interval_width_quartile","Q3",1868,1641,0.87847965738758,0.387666656091502,0.460880414112048,4.06477516059957
+2,"interval_width_quartile","Q3",3880,3252,0.838144329896907,0.389151519326864,0.455512300134519,3.92216494845361
+1,"interval_width_quartile","Q4",1753,1495,0.85282373074729,0.522398307318929,0.463810195053108,2.18824871648602
+2,"interval_width_quartile","Q4",3996,3465,0.867117117117117,0.510368641582237,0.479130318116811,2.24374374374374

validation/outputs/ppc/uncertainty_cic_80pct_summary_2026-08-12_16-50-24.csv

@@ -0,0 +1,3 @@
+dimension,cic,cic_pct,ci_lower,ci_upper,n,covered
+economic_lr,0.7585513078470825,75.9,0.7487048592332345,0.7681314280529844,7455,5655
+galtan,0.6785507432910741,67.9,0.6711640763491561,0.6858491483111274,15539,10544

validation/outputs/ppc/uncertainty_cic_summary_2026-08-12_16-50-24.csv

@@ -0,0 +1,3 @@
+dimension,cic,cic_pct,ci_lower,ci_upper,n,covered
+economic_lr,0.8984574111334674,89.8,0.8913943427959263,0.9051100366508769,7455,6698
+galtan,0.8475448870583693,84.8,0.8418071013756855,0.8531108729540536,15539,13170

validation/outputs/ppc/uncertainty_economic_lr_by_project_2026-08-12_16-50-24.csv

@@ -0,0 +1,5 @@
+project,n,covered,cic
+V-Party,5645,4940,0.87511071744907
+CHES,1177,1145,0.9728122344944775
+POPPA,384,379,0.9869791666666666
+GPS,249,234,0.9397590361445783

validation/outputs/ppc/uncertainty_galtan_by_project_2026-08-12_16-50-24.csv

@@ -0,0 +1,4 @@
+project,n,covered,cic
+V-Party,14114,11834,0.8384582683859997
+CHES,1176,1109,0.9430272108843537
+GPS,249,227,0.9116465863453815

validation/outputs/source_support_interactions.csv

@@ -0,0 +1,29 @@
+"dimension","group_type","group","comparison","group_n","text_only_n","estimate","approximate_se","ci_lower","ci_upper","model_n","singular_fit"
+"economic_lr","region","Asia-Pacific","Text only",414,157,0.023499613577582,0.0194884624938891,-0.0146977729104407,0.0616970000656046,4918,FALSE
+"economic_lr","region","Europe","Text only",3518,1150,0.0234974462379077,0.019488462450132,-0.0146999401643511,0.0616948326401665,4918,FALSE
+"economic_lr","region","Latin America","Text only",527,178,0.0234982664429087,0.0194884625013502,-0.0146991200597376,0.0616956529455551,4918,FALSE
+"economic_lr","region","North America","Text only",182,65,0.0234984096509199,0.0194884625063241,-0.0146989768614752,0.0616957961633151,4918,FALSE
+"economic_lr","region","Other","Text only",277,108,0.0234999353063372,0.0194884625020575,-0.0146974511976955,0.0616973218103699,4918,FALSE
+"economic_lr","decade","1940","Text only",151,151,0.0361702336962241,0.0264169933989952,-0.0156070733658065,0.0879475407582547,4918,FALSE
+"economic_lr","decade","1950","Text only",290,288,0.0515452173138878,0.0237789826712282,0.00493841127828048,0.0981520233494951,4918,FALSE
+"economic_lr","decade","1960","Text only",310,301,0.0173768258790806,0.0236362916289398,-0.0289503057136415,0.0637039574718027,4918,FALSE
+"economic_lr","decade","1970","Text only",468,118,0.0318158385898869,0.0278251134588641,-0.0227213837894868,0.0863530609692606,4918,FALSE
+"economic_lr","decade","1980","Text only",537,164,0.0811017672950212,0.0260804140172057,0.029984155821298,0.132219378768744,4918,FALSE
+"economic_lr","decade","1990","Text only",961,242,0.090351969036333,0.0244789559738209,0.042373215327644,0.138330722745022,4918,FALSE
+"economic_lr","decade","2000","Text only",966,157,0.00199794203598232,0.026319290732969,-0.0495878678006369,0.0535837518726016,4918,FALSE
+"economic_lr","decade","2010","Text only",1019,169,-0.0377271288552299,0.0260831159721564,-0.0888500361606564,0.0133957784501965,4918,FALSE
+"economic_lr","decade","2020","Text only",216,68,-0.0611440568032739,0.031816463272324,-0.123504324817029,0.00121621121048109,4918,FALSE
+"galtan","region","Asia-Pacific","Text only",414,157,-0.0583506874216767,0.0255289688453339,-0.108387466358531,-0.00831390848482223,4918,FALSE
+"galtan","region","Europe","Text only",3518,1150,0.0280310752712225,0.0215623111509888,-0.0142310545847155,0.0702932051271606,4918,FALSE
+"galtan","region","Latin America","Text only",527,178,-0.0274044614386924,0.0276182311152612,-0.0815361944246044,0.0267272715472196,4918,FALSE
+"galtan","region","North America","Text only",182,65,-0.0347638802349499,0.0292448253676695,-0.0920837379555821,0.0225559774856824,4918,FALSE
+"galtan","region","Other","Text only",277,108,0.00122806156533374,0.0274679779619373,-0.0526091752400633,0.0550652983707308,4918,FALSE
+"galtan","decade","1940","Text only",151,151,-0.00886963742385274,0.0230507690251292,-0.0540491447131059,0.0363098698654004,4918,FALSE
+"galtan","decade","1950","Text only",290,288,-0.0261254372163238,0.0223317588503254,-0.0698956845629617,0.017644810130314,4918,FALSE
+"galtan","decade","1960","Text only",310,301,-0.0423163966136925,0.0222910716791448,-0.0860068971048164,0.00137410387743137,4918,FALSE
+"galtan","decade","1970","Text only",468,118,0.00112335073405384,0.0233940571942832,-0.0447290013667412,0.0469757028348488,4918,FALSE
+"galtan","decade","1980","Text only",537,164,-0.0143718371416263,0.0229441471113722,-0.0593423654799158,0.0305986911966632,4918,FALSE
+"galtan","decade","1990","Text only",961,242,-0.0166570316482948,0.0225348597781818,-0.060825356813531,0.0275112935169415,4918,FALSE
+"galtan","decade","2000","Text only",966,157,-0.00235707430082799,0.0230142737432502,-0.0474650508375983,0.0427509022359423,4918,FALSE
+"galtan","decade","2010","Text only",1019,169,-0.0327916901242996,0.0229562136417423,-0.0777858688621145,0.0122024886135154,4918,FALSE
+"galtan","decade","2020","Text only",216,68,-0.0219020523308627,0.0242056730938077,-0.0693451715947258,0.0255410669330004,4918,FALSE

validation/outputs/source_support_pooled.csv

@@ -0,0 +1,7 @@
+"dimension","comparison","category_n","model_n","estimate","clustered_se","ci_lower","ci_upper","p_value"
+"economic_lr","Text only",1658,4918,0.0146213824427815,0.0194180161980694,-0.0234379293054345,0.0526806941909975,0.451461789942091
+"economic_lr","Expert only",237,4918,-0.0151752788922033,0.0345235882210866,-0.082841511805533,0.0524909540211264,0.660253918863865
+"economic_lr","Temporal propagation",84,4918,-0.0507326635318485,0.0585015466678104,-0.165395695000757,0.0639303679370598,0.385831291398033
+"galtan","Text only",1658,4918,0.0178370523352578,0.0203365901149711,-0.0220226642900856,0.0576967689606011,0.380436887567239
+"galtan","Expert only",237,4918,0.03967989229365,0.0177110608676138,0.00496621299312706,0.074393571594173,0.0250648508349581
+"galtan","Temporal propagation",84,4918,-0.0451312946088707,0.0794792780909196,-0.200910679667073,0.110648090449332,0.570145484504212

validation/outputs/vparty_sensitivity_groups.csv

@@ -0,0 +1,187 @@
+"dimension","group_type","group","n","parties","mean_abs_difference","median_abs_difference","p90_abs_difference","p95_abs_difference","mean_signed_difference","mean_interval_width_production","mean_interval_width_no_vparty","mean_interval_width_change"
+"economic_lr","country","AL",42,9,0.11007115056994,0.1158001133125,0.16388615046625,0.18221207556125,0.109997455530655,0.313384542380952,0.300286733928571,-0.0130978084523809
+"economic_lr","country","AM",26,8,0.136577217672115,0.150937754875,0.2098864180625,0.2208015980625,-0.113680095763462,0.273092678461538,0.325743899807692,0.0526512213461538
+"economic_lr","country","AR",22,4,0.0848245688585227,0.0528036060625,0.1669706491625,0.24947483924125,-0.0285713360403409,0.273925635227273,0.321399650568182,0.0474740153409091
+"economic_lr","country","AT",84,7,0.0357452832340774,0.0176101082499999,0.0902926514125,0.113726891864375,0.0259106200822917,0.198733178065476,0.1805706,-0.0181625780654762
+"economic_lr","country","AU",123,9,0.0389789289578354,0.0426316946250002,0.0623188192950001,0.06583370092125,-0.0307516726874898,0.213811405,0.209957416239837,-0.0038539887601626
+"economic_lr","country","AZ",6,3,0.107751395166667,0.0686461146875,0.21379690875,0.21823543815,-0.0978946908333333,0.294933266666667,0.35058399875,0.0556507320833335
+"economic_lr","country","BA",59,9,0.183242221828814,0.189491590375,0.2311323814975,0.23891121964625,-0.183242221828814,0.277945797457627,0.288832576949152,0.0108867794915254
+"economic_lr","country","BE",192,20,0.0471656297852865,0.03839504868125,0.10255722729375,0.142219740750625,0.0341510484701823,0.230366942808594,0.228406965273437,-0.00195997753515626
+"economic_lr","country","BG",43,12,0.0436524454482558,0.035510602375,0.0840780225500001,0.11829644133,0.0381575565238372,0.199575343837209,0.194593854011628,-0.00498148982558139
+"economic_lr","country","BO",2,1,0.0152565791125,0.0152565791125,0.0213876231425,0.02215400364625,-0.00766380503750001,0.2408585175,0.2971823675,0.0563238499999999
+"economic_lr","country","BR",57,6,0.0909301161991667,0.0724382820374999,0.1837030251575,0.22917528408,-0.0309207419535965,0.293843987236842,0.592666575657895,0.298822588421053
+"economic_lr","country","CA",103,8,0.0797441370808253,0.0746296091249999,0.130909846205,0.14530870479,0.0797441370808253,0.213273919902913,0.213908178883495,0.000634258980582522
+"economic_lr","country","CH",155,16,0.0620204564002177,0.0753673375,0.09888814595,0.112733779525,-0.0580914079873306,0.222262379562903,0.204325304451613,-0.0179370751112903
+"economic_lr","country","CL",65,9,0.0985104462301923,0.0795227623500002,0.1946132834425,0.2461950214025,-0.0175779041278846,0.319459472346154,0.510955569961538,0.191496097615385
+"economic_lr","country","CO",19,7,0.171026812829342,0.145873909225,0.353764815529,0.370583245686,-0.169040405891184,0.339858258552632,0.329198671947368,-0.0106595866052632
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+"galtan","country","MX",86,10,0.0969902280651163,0.07772093853125,0.217417330625,0.2395366675625,0.064741362575,0.158348759883721,0.326388927063953,0.168040167180233
+"galtan","country","NL",188,23,0.0521148246466423,0.046166573225,0.094267162875,0.129523389143125,0.00171048192835771,0.192712959401596,0.252123994095745,0.0594110346941489
+"galtan","country","NO",129,10,0.0745414090338663,0.0700747067499998,0.139510584715,0.174004472485,-0.0318034112825097,0.219965351782946,0.346935125155039,0.126969773372093
+"galtan","country","NZ",106,10,0.149573495223874,0.1465860698125,0.284350521875,0.32715412284375,0.140047107755136,0.201643114929245,0.325226359551887,0.123583244622641
+"galtan","country","PA",9,3,0.0301984971919445,0.018087274625,0.0720320454755,0.0879095694265,-0.00293236099749997,0.247901383333333,0.386599255277778,0.138697871944444
+"galtan","country","PE",5,3,0.019958924,0.017818636125,0.029449596775,0.033131676075,0.00119512489999998,0.18333602,0.440814305,0.257478285
+"galtan","country","PL",47,11,0.0583398298868351,0.04899842563,0.11508536858,0.1400047581175,-0.0288536495857713,0.194182434095745,0.286884986531915,0.0927025524361702
+"galtan","country","PT",120,15,0.0589013840949583,0.0530732530625,0.10871108691,0.1319234160725,0.028491267907125,0.2365999399375,0.369802342895833,0.133202402958333
+"galtan","country","RO",31,9,0.101976054703226,0.062737579125,0.269870755475,0.281506620775,-0.0759611564129032,0.173238150806452,0.273333795806452,0.100095645
+"galtan","country","RS",78,18,0.0566899753289423,0.04838573486875,0.12882280397875,0.14894020068125,0.0313228374658975,0.203541225737179,0.341410328205128,0.137869102467949
+"galtan","country","RU",31,10,0.0823772725173387,0.0706485935000001,0.177669660625,0.1911726254375,0.0742536083076613,0.166878729032258,0.333044997580645,0.166166268548387
+"galtan","country","SE",144,8,0.0779477337775087,0.065282401925,0.14971096477,0.192713488666437,0.00760519412474827,0.197623276006944,0.293993233263889,0.0963699572569444
+"galtan","country","SI",65,15,0.0661264041638461,0.0445719423750001,0.16110350395,0.1988129118445,-0.0247239681084615,0.188567015,0.291528464115385,0.102961449115385
+"galtan","country","SK",54,14,0.0502516793186806,0.0501847275000001,0.102417474269625,0.12425404441275,0.00749116253780092,0.167382486435185,0.229228685092593,0.0618461986574074
+"galtan","country","TR",61,12,0.073720984571373,0.0533070427500001,0.15832579625,0.179094610925,-0.0121620851246516,0.19658379307377,0.34434646397541,0.147762670901639
+"galtan","country","UA",34,11,0.113488601584559,0.1119919954375,0.1939840840375,0.2112209067625,0.0833224104154412,0.164413496323529,0.328120980147059,0.163707483823529
+"galtan","country","US",78,2,0.0570046725294872,0.045147282,0.1233196885425,0.1297916708125,-0.0086567002576923,0.177735137820513,0.403358578205128,0.225623440384615
+"galtan","country","UY",2,2,0.13808531583125,0.13808531583125,0.17447075566625,0.179018935645625,0.13808531583125,0.17999905,0.4755078875,0.2955088375
+"galtan","country","ZA",22,6,0.09775905798125,0.09211542475,0.1806953853375,0.2005182710625,0.0946672932982955,0.174702580681818,0.340332871590909,0.165630290909091
+"economic_lr","decade","1940",151,92,0.0371816203678808,0.0292069454999998,0.0742582218750001,0.0912451055624999,-0.000173471055629137,0.189821346683775,0.172437360596026,-0.0173839860877483
+"economic_lr","decade","1950",290,103,0.0407176971207224,0.033865868875,0.086362646754875,0.09896772440375,-0.00778987169985517,0.202589561812931,0.189039881362931,-0.01354968045
+"economic_lr","decade","1960",310,120,0.0427885898915454,0.034330352925,0.0896977708749999,0.122006477058125,-0.0025685449944546,0.220079714312097,0.211818625971774,-0.00826108834032258
+"economic_lr","decade","1970",440,153,0.0502866782806628,0.0314642366875,0.118349408698125,0.149890328475,0.00329100990037017,0.226482275239773,0.231859487863636,0.00537721262386364
+"economic_lr","decade","1980",454,185,0.05307470873144,0.0412179630312499,0.119116974148,0.1588293852875,0.00790578806827919,0.237694862507159,0.249603410189427,0.0119085476822687
+"economic_lr","decade","1990",873,371,0.0641986936875508,0.0464593551375,0.14472205606,0.17828758647,0.00287533560658863,0.248898039122279,0.274924678699885,0.026026639577606
+"economic_lr","decade","2000",900,411,0.0631149535508028,0.0418411898125,0.14411636095,0.20609396516125,-0.0182889110804333,0.238621175438889,0.263199891986111,0.0245787165472222
+"economic_lr","decade","2010",995,450,0.0538611624244023,0.034718012125,0.127826655375,0.1804660809125,-0.0221962105316555,0.215534625020854,0.222818895102513,0.00728427008165828
+"economic_lr","decade","2020",216,180,0.0371307902267448,0.029627738125,0.08012091931875,0.112391917778125,-0.0012710929483941,0.226797810039352,0.221887160357639,-0.00491064968171296
+"galtan","decade","1940",151,92,0.0708290557321523,0.0562137547875,0.14081580895,0.1835157929375,0.0261701161742053,0.263486062102649,0.35838113705298,0.0948950749503311
+"galtan","decade","1950",290,103,0.0759758618539849,0.058937112361875,0.17380860064,0.23190050096875,0.0206251947466875,0.259375958672414,0.354083490781896,0.0947075321094827
+"galtan","decade","1960",310,120,0.0843626901791028,0.0676845134375,0.166080884235,0.22069010237,0.0019402298805746,0.25841617908871,0.366428205235484,0.108012026146774
+"galtan","decade","1970",440,153,0.0986147156691975,0.0788397242125,0.20555214061875,0.25203876564375,-0.0108637360172941,0.221199917153409,0.354920847494886,0.133720930341477
+"galtan","decade","1980",454,185,0.0808016044734364,0.058811841435,0.173072975875,0.2204824547125,-0.000359151480391218,0.22031273981663,0.368595810490529,0.148283070673899
+"galtan","decade","1990",873,371,0.0873413504149248,0.0643135031250001,0.19577037354,0.2490063037465,0.0102924923551869,0.198413997671821,0.353932825577892,0.155518827906071
+"galtan","decade","2000",900,411,0.0798759182483464,0.0574670300500001,0.1859949581375,0.23767782646875,0.0291185818013869,0.191728931302222,0.341912218414444,0.150183287112222
+"galtan","decade","2010",995,450,0.0672717050724113,0.0496686596250002,0.15014200965,0.2015970361125,0.0356412896617276,0.177604650279648,0.294148019771859,0.116543369492211
+"galtan","decade","2020",216,180,0.0589089367769583,0.0502599744375,0.1234101119375,0.1486720144375,0.0364686091928843,0.210231471782407,0.306094965694444,0.095863493912037
+"economic_lr","region","Asia-Pacific",399,45,0.0571219334616526,0.0443660183749999,0.12416155903,0.187599069753375,-0.0145487249648042,0.244419383032581,0.260598511447368,0.016179128414787
+"economic_lr","region","Europe",3434,516,0.0464743120210818,0.0326595713125001,0.1039714675125,0.141266804764375,0.000913014723246237,0.221488947357091,0.217554286148733,-0.0039346612083576
+"economic_lr","region","Latin America",356,63,0.117336302214344,0.0935154758875001,0.2506860608625,0.314390097832188,-0.0753651143393638,0.272504908265449,0.414404904780899,0.141899996515449
+"economic_lr","region","North America",181,10,0.075989400744268,0.0697219131249999,0.12174174215,0.1408082874125,0.0147691199112569,0.22178018121547,0.248985111878453,0.0272049306629834
+"economic_lr","region","Other",259,50,0.0519685596374595,0.034531058875,0.13885551671,0.173381673425,-0.0290635234477239,0.244889429002896,0.249916611911197,0.00502718290830117
+"galtan","region","Asia-Pacific",399,45,0.17313647407231,0.164663835875,0.30758002685,0.3407870748375,0.113936503536769,0.247357245119048,0.397836865076441,0.150479619957393
+"galtan","region","Europe",3434,516,0.0685237842799692,0.0549442329875,0.14452337820625,0.18327854436375,0.00751445234684062,0.207398450992065,0.318306419611881,0.110907968619817
+"galtan","region","Latin America",356,63,0.084127775017306,0.061753621071875,0.208029392375,0.24112464406,0.0240835342113994,0.195054616369382,0.53504948869382,0.339994872324438
+"galtan","region","North America",181,10,0.0694766852943992,0.0565289545375,0.1508847962125,0.1849990895,0.0207572464072583,0.188053857859116,0.352223026671271,0.164169168812155
+"galtan","region","Other",259,50,0.0765835156139755,0.06323538313625,0.147387130125,0.1813591133375,0.00203723883911333,0.177542301921815,0.244660942685328,0.0671186407635135
+"economic_lr","family_label","Agrarian",152,12,0.0320538710685033,0.0186717304812501,0.0889848963500001,0.0970678048749999,-0.00593539169876645,0.218227728157895,0.206365923651316,-0.0118618045065789
+"economic_lr","family_label","Christian Democratic",433,39,0.0375033907769919,0.0319368376249999,0.0720206793249999,0.096269401725,-0.0131147804376732,0.23566491443418,0.231489054676674,-0.00417585975750576
+"economic_lr","family_label","Communist/Far Left",334,48,0.0486644586896976,0.03529525582875,0.10571990613575,0.146860105575,0.0479768514953638,0.155147337715569,0.181992471565868,0.0268451338502994
+"economic_lr","family_label","Conservative",646,82,0.037674156340044,0.0327651997500001,0.066197402125,0.084930110375,-0.0193102770752036,0.211213435830108,0.202289049549923,-0.00892438628018576
+"economic_lr","family_label","Green/Ecological",187,29,0.0429328687294586,0.032739016375,0.087422558962,0.0979384956524999,0.0348435589150869,0.215338299772727,0.213619454786096,-0.00171884498663102
+"economic_lr","family_label","Liberal",576,81,0.0306596129757378,0.024781423125,0.0650074390624999,0.0774783831562499,-0.0101040985020399,0.204604762469618,0.183489200043403,-0.0211155624262153
+"economic_lr","family_label","Other",9,2,0.0133180642291667,0.00946586175000003,0.0245744710500001,0.0245800339000001,-0.0047759738402778,0.223425625,0.215880991666667,-0.00754463333333332
+"economic_lr","family_label","Radical Right",257,50,0.0448900349409387,0.0356326438000001,0.0914339091749999,0.1437146432,-0.0339084273444309,0.239075980359922,0.230133361410506,-0.00894261894941635
+"economic_lr","family_label","Social Democratic",761,86,0.0487425635779379,0.0369901536250001,0.1067683898125,0.12994591825,0.0385901765304346,0.223349851519382,0.220255219645204,-0.00309463187417871
+"economic_lr","family_label","Special issue",143,25,0.026883424939528,0.0265684387,0.04679836750075,0.0633105665737501,0.0149538624225699,0.251831107465035,0.231985332972028,-0.019845774493007
+"economic_lr","family_label","Unclassified",1131,230,0.0984837351550462,0.0795355523749999,0.20745169625,0.2510335748,-0.048423238788916,0.272029213213086,0.33404468710389,0.0620154738908046
+"galtan","family_label","Agrarian",152,12,0.105141823237155,0.0907771423749999,0.1925025656875,0.2213469184625,0.0604336606998191,0.205144194572368,0.29353910087171,0.0883949062993421
+"galtan","family_label","Christian Democratic",433,39,0.0718250720421449,0.0555694713875,0.14215453213,0.1800220496625,-0.0131192834506264,0.199758031241339,0.317671331662817,0.117913300421478
+"galtan","family_label","Communist/Far Left",334,48,0.074038310086303,0.0547699002059375,0.162768573325,0.20951525864375,0.0405196851759574,0.235479590085329,0.349722435327695,0.114242845242365
+"galtan","family_label","Conservative",646,82,0.0983403746741395,0.0776775026875,0.220572271925,0.277122060828125,0.0441218983917196,0.211539288099845,0.335541979530573,0.124002691430728
+"galtan","family_label","Green/Ecological",187,29,0.0509116802868449,0.0313170713125,0.123042502875,0.1889626426,0.0157630846182353,0.166198087125668,0.251023559705882,0.0848254725802139
+"galtan","family_label","Liberal",576,81,0.0697021356713967,0.0542307168125,0.14791305325,0.181032452013125,0.00526827062959288,0.218189066197917,0.323725934127604,0.105536867929687
+"galtan","family_label","Other",9,2,0.0644653042833333,0.0705062719999999,0.0904335059499998,0.0908028820999998,0.0333764893277777,0.147571191666667,0.227039952777778,0.0794687611111111
+"galtan","family_label","Radical Right",257,50,0.0914724143323103,0.0673691042499999,0.21891551714,0.2641599179,0.0488177438893531,0.197410741536965,0.232469848920233,0.0350591073832685
+"galtan","family_label","Social Democratic",761,86,0.080105907141705,0.057859077375,0.1730134955475,0.2356013771625,0.0161545037538272,0.207177564181997,0.324027555488173,0.116849991306176
+"galtan","family_label","Special issue",143,25,0.0534221270324589,0.04043228925,0.10993480277,0.148755378370375,-0.00603089098020367,0.239392236737762,0.291600286743007,0.0522080500052448
+"galtan","family_label","Unclassified",1131,230,0.0787409936722545,0.058680683995,0.183507806055,0.23208491015,0.00735828626895603,0.200412819792219,0.415722207560787,0.215309387768568
+"economic_lr","source_support_class","both_direct_or_nearby",2898,572,0.0562311585382358,0.0382146124375001,0.1329329555875,0.173217369964375,-0.00584478889702773,0.215599624201087,0.225627670403468,0.0100280462023809
+"economic_lr","source_support_class","expert_only_direct_or_nearby",12,11,0.034192924803125,0.022058564,0.07781032056875,0.08212848558875,0.012055191359375,0.24052055625,0.260045897916667,0.0195253416666667
+"economic_lr","source_support_class","temporal_propagation",62,17,0.101701728419577,0.049888057674375,0.2394404627875,0.312558430265,-0.0740730612826412,0.251171412419355,0.286950409475806,0.0357789970564516
+"economic_lr","source_support_class","text_only_direct_or_nearby",1657,377,0.0493037907647899,0.03583326690375,0.110562605595,0.1447094538,-0.00782607456147747,0.250712200439801,0.26167930811557,0.0109671076757695
+"galtan","source_support_class","both_direct_or_nearby",2898,572,0.0831440902163216,0.0609966990937501,0.18544264335,0.24018766278125,0.0212481470172447,0.18549785362431,0.317582449142512,0.132084595518202
+"galtan","source_support_class","expert_only_direct_or_nearby",12,11,0.0483296314947917,0.0535480428125,0.0841788765275,0.090728015821875,0.0139677355864583,0.19445685,0.368919924583333,0.174463074583333
+"galtan","source_support_class","temporal_propagation",62,17,0.109246388097642,0.088083927,0.2361952786125,0.2423211530625,0.0324953038705036,0.156761379870968,0.244535198866935,0.0877738189959677
+"galtan","source_support_class","text_only_direct_or_nearby",1657,377,0.0714829340819805,0.058025915,0.14154745872,0.20812918545,0.0122898534614001,0.247879904557483,0.379876985534671,0.131997080977188

validation/outputs/vparty_sensitivity_top_cases.csv

@@ -0,0 +1,61 @@
+"dimension","party_id","party_name","party_short","country","year","production","no_vparty","signed_difference","absolute_difference","production_interval_width","no_vparty_interval_width","source_support_class","family"
+"economic_lr",446,"Mexican Green Ecologist Party","PVEM","MX",2006,0.78331803875,0.302941706325,-0.480376332425,0.480376332425,0.198701025,0.3971359,"text_only_direct_or_nearby","Unclassified"
+"economic_lr",446,"Mexican Green Ecologist Party","PVEM","MX",2012,0.760635541875,0.2937373089375,-0.4668982329375,0.4668982329375,0.2009882,0.327665875,"text_only_direct_or_nearby","Unclassified"
+"economic_lr",446,"Mexican Green Ecologist Party","PVEM","MX",2009,0.771687653,0.3095788204125,-0.4621088325875,0.4621088325875,0.1954599,0.351576625,"text_only_direct_or_nearby","Unclassified"
+"economic_lr",446,"Mexican Green Ecologist Party","PVEM","MX",2003,0.780395189375,0.33433866755,-0.446056521825,0.446056521825,0.21091025,0.424579275,"temporal_propagation","Unclassified"
+"economic_lr",446,"Mexican Green Ecologist Party","PVEM","MX",2015,0.765128507125,0.3376274813875,-0.4275010257375,0.4275010257375,0.1936747,0.33989315,"temporal_propagation","Unclassified"
+"economic_lr",1577,"Colombian Conservative Party","PCC","CO",2010,0.839930205875,0.428090524625,-0.41183968125,0.41183968125,0.16999555,0.367937675,"both_direct_or_nearby","Unclassified"
+"economic_lr",4373,"Justice Party","AP","TR",1977,0.662219564625,0.2898449586625,-0.3723746059625,0.3723746059625,0.333305125,0.480909195,"both_direct_or_nearby","Unclassified"
+"economic_lr",827,"Pachakutik Plurinational Unity Movement - New Country","MUPP-NP","EC",2002,0.13025843815875,0.49914328630125,0.3688848481425,0.3688848481425,0.1970410525,0.845954685,"both_direct_or_nearby","Unclassified"
+"economic_lr",827,"Pachakutik Plurinational Unity Movement - New Country","MUPP-NP","EC",2006,0.129486856525,0.49800719580125,0.36852033927625,0.36852033927625,0.216157335,0.874626755,"both_direct_or_nearby","Unclassified"
+"economic_lr",362,"Colombian Liberal Party","PLC","CO",2010,0.44652740975,0.08052821246,-0.36599919729,0.36599919729,0.296508575,0.251284637,"both_direct_or_nearby","Unclassified"
+"economic_lr",827,"Pachakutik Plurinational Unity Movement - New Country","MUPP-NP","EC",1998,0.137947749675,0.49969208406875,0.36174433439375,0.36174433439375,0.1949734425,0.8040779475,"both_direct_or_nearby","Unclassified"
+"economic_lr",2307,"New Frontier Party","NFP","KR",2004,0.78653404975,0.42563784325,-0.3608962065,0.3608962065,0.20216155,0.335555875,"both_direct_or_nearby","Unclassified"
+"economic_lr",2307,"New Frontier Party","NFP","KR",2012,0.748232659375,0.393992868,-0.354239791375,0.354239791375,0.2158533,0.30876265,"both_direct_or_nearby","Unclassified"
+"economic_lr",431,"Democratic Party of Albanians","DPA/PDSh","MK",2006,0.624241323125,0.270382054725,-0.3538592684,0.3538592684,0.2778642,0.360123525,"both_direct_or_nearby","Unclassified"
+"economic_lr",827,"Pachakutik Plurinational Unity Movement - New Country","MUPP-NP","EC",1996,0.1474476632125,0.49872718705,0.3512795238375,0.3512795238375,0.2150713825,0.783277925,"both_direct_or_nearby","Unclassified"
+"economic_lr",362,"Colombian Liberal Party","PLC","CO",2006,0.43398183575,0.08327561566125,-0.35070622008875,0.35070622008875,0.303243975,0.2337936175,"both_direct_or_nearby","Unclassified"
+"economic_lr",1474,"Institutional Revolutionary Party","PRI","MX",2012,0.694937151875,0.36462675225,-0.330310399625,0.330310399625,0.21584705,0.333578775,"text_only_direct_or_nearby","Unclassified"
+"economic_lr",1474,"Institutional Revolutionary Party","PRI","MX",2006,0.7406987215,0.4108210867625,-0.3298776347375,0.3298776347375,0.216189275,0.3970175,"text_only_direct_or_nearby","Unclassified"
+"economic_lr",362,"Colombian Liberal Party","PLC","CO",1998,0.471503588875,0.14373430549125,-0.32776928338375,0.32776928338375,0.30879815,0.3470966,"both_direct_or_nearby","Unclassified"
+"economic_lr",1599,"Independent Democratic Union","UDI","CL",2005,0.846619912375,0.520464961775,-0.3261549506,0.3261549506,0.17401525,0.4490288,"temporal_propagation","Unclassified"
+"economic_lr",5879,"Democratic Center","CD","CO",2014,0.82792676,0.5027692285,-0.3251575315,0.3251575315,0.18418935,0.346853475,"both_direct_or_nearby","Unclassified"
+"economic_lr",1674,"VMRO-DPMNE","VMRO-DPMNE","MK",2002,0.747142577125,0.422961616525,-0.3241809606,0.3241809606,0.2459292,0.394813975,"both_direct_or_nearby","Unclassified"
+"economic_lr",1190,"Motherland Party","ANAP","TR",1999,0.685513704,0.3654118114875,-0.3201018925125,0.3201018925125,0.26022905,0.342326225,"both_direct_or_nearby","Conservative"
+"economic_lr",362,"Colombian Liberal Party","PLC","CO",2002,0.407775975,0.08808654867125,-0.31968942632875,0.31968942632875,0.287786025,0.21200674,"both_direct_or_nearby","Unclassified"
+"economic_lr",815,"National Salvation Party","MSP","TR",1977,0.63945227325,0.3198877530625,-0.3195645201875,0.3195645201875,0.3167729,0.423142125,"both_direct_or_nearby","Unclassified"
+"economic_lr",4373,"Justice Party","AP","TR",1973,0.645414913,0.3320327251625,-0.3133821878375,0.3133821878375,0.2996928,0.41292565,"both_direct_or_nearby","Unclassified"
+"economic_lr",446,"Mexican Green Ecologist Party","PVEM","MX",2018,0.724924867125,0.412301212125,-0.312623655,0.312623655,0.196557225,0.289393075,"temporal_propagation","Unclassified"
+"economic_lr",446,"Mexican Green Ecologist Party","PVEM","MX",1997,0.68345350075,0.37213434045,-0.3113191603,0.3113191603,0.373734625,0.4293999,"temporal_propagation","Unclassified"
+"economic_lr",4405,"Liberal Party","PR","BR",2010,0.805440906125,0.4959839998,-0.309456906325,0.309456906325,0.22779515,0.6460089,"text_only_direct_or_nearby","Unclassified"
+"economic_lr",1474,"Institutional Revolutionary Party","PRI","MX",2009,0.70526815875,0.397337176125,-0.307930982625,0.307930982625,0.204080825,0.323306525,"text_only_direct_or_nearby","Unclassified"
+"galtan",990,"Australian Democrats","AD","AU",2001,0.338562057,0.85272370525,0.51416164825,0.51416164825,0.214476075,0.243330675,"both_direct_or_nearby","Social Democratic"
+"galtan",990,"Australian Democrats","AD","AU",1998,0.413618905125,0.906091827,0.492472921875,0.492472921875,0.181907525,0.13750485,"both_direct_or_nearby","Social Democratic"
+"galtan",990,"Australian Democrats","AD","AU",1996,0.36119623075,0.84415619475,0.482959964,0.482959964,0.1871557,0.19650735,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",1993,0.31780245925,0.787947682375,0.470145223125,0.470145223125,0.22103085,0.401841475,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",1998,0.43088434725,0.90042605275,0.4695417055,0.4695417055,0.192973425,0.158486825,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",1990,0.318993482875,0.78831252875,0.469319045875,0.469319045875,0.221467575,0.42828265,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",1987,0.332828033,0.78906332725,0.45623529425,0.45623529425,0.216371075,0.384759775,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",2001,0.479487656,0.929329388125,0.449841732125,0.449841732125,0.19050665,0.12066555,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",2007,0.3671275265,0.81184206075,0.44471453425,0.44471453425,0.191143825,0.24396915,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",1996,0.35332868725,0.79386454675,0.4405358595,0.4405358595,0.190665825,0.267057925,"both_direct_or_nearby","Social Democratic"
+"galtan",424,"Australian Labor Party","ALP","AU",2004,0.454032172375,0.894014407375,0.439982235,0.439982235,0.177285525,0.1378716,"both_direct_or_nearby","Social Democratic"
+"galtan",1966,"United National Party","UNP","LK",1970,0.67440380725,0.235886241525,-0.438517565725,0.438517565725,0.181673275,0.3844404475,"both_direct_or_nearby","Unclassified"
+"galtan",424,"Australian Labor Party","ALP","AU",1984,0.37090576025,0.790738644625,0.419832884375,0.419832884375,0.185054675,0.244114425,"both_direct_or_nearby","Social Democratic"
+"galtan",1075,"United Torah Judaism","UTJ","IL",2009,0.646416692625,0.22733472837125,-0.41908196425375,0.41908196425375,0.337046125,0.6495289825,"text_only_direct_or_nearby","Conservative"
+"galtan",1221,"League","L","IT",1992,0.72339442825,0.3233998345125,-0.3999945937375,0.3999945937375,0.1864393,0.43572915,"both_direct_or_nearby","Radical Right"
+"galtan",424,"Australian Labor Party","ALP","AU",1983,0.3458623055,0.737733647875,0.391871342375,0.391871342375,0.18553985,0.31302215,"both_direct_or_nearby","Social Democratic"
+"galtan",1221,"League","L","IT",1994,0.732691392,0.350859984775,-0.381831407225,0.381831407225,0.172051275,0.4147524,"both_direct_or_nearby","Radical Right"
+"galtan",1049,"New Zealand Labour Party","Labour","NZ",2011,0.307176632875,0.687613138625,0.38043650575,0.38043650575,0.188001825,0.39095055,"both_direct_or_nearby","Social Democratic"
+"galtan",1697,"Hungarian Democratic Forum","MDF","HU",1990,0.5820039805,0.20511058695,-0.37689339355,0.37689339355,0.191749875,0.3194354275,"both_direct_or_nearby","Conservative"
+"galtan",1998,"Liberal Party","LP","AU",1969,0.414252437125,0.78124611825,0.366993681125,0.366993681125,0.30239525,0.340995525,"text_only_direct_or_nearby","Conservative"
+"galtan",284,"Unified Democratic Coalition","CDU","PT",2011,0.286767969375,0.6525784155,0.365810446125,0.365810446125,0.216958075,0.3049402,"both_direct_or_nearby","Communist/Far Left"
+"galtan",1824,"New Zealand National Party","National","NZ",2002,0.518077548125,0.883790847375,0.36571329925,0.36571329925,0.193445475,0.197484975,"both_direct_or_nearby","Conservative"
+"galtan",622,"Christian Democratic and Flemish","CD&V","BE",1974,0.532628898625,0.173078888215,-0.35955001041,0.35955001041,0.247636825,0.4395033325,"text_only_direct_or_nearby","Christian Democratic"
+"galtan",194,"Social Christian Reformist Party","PRSC","DO",1996,0.861104359125,0.5033975994125,-0.3577067597125,0.3577067597125,0.12461655,0.8503037775,"both_direct_or_nearby","Unclassified"
+"galtan",285,"Liberal National Party of Queensland","LNP","AU",2016,0.48517569125,0.842698982125,0.357523290875,0.357523290875,0.226642625,0.252878475,"both_direct_or_nearby","Liberal"
+"galtan",1824,"New Zealand National Party","National","NZ",2008,0.4818931305,0.83653531875,0.35464218825,0.35464218825,0.186821275,0.2409097,"both_direct_or_nearby","Conservative"
+"galtan",1998,"Liberal Party","LP","AU",1958,0.420013153625,0.770361895625,0.350348742,0.350348742,0.229623975,0.2478383,"text_only_direct_or_nearby","Conservative"
+"galtan",194,"Social Christian Reformist Party","PRSC","DO",1998,0.853912629125,0.50421325068375,-0.34969937844125,0.34969937844125,0.119754,0.8840918725,"both_direct_or_nearby","Unclassified"
+"galtan",622,"Christian Democratic and Flemish","CD&V","BE",1971,0.5433591915,0.19802355463375,-0.34533563686625,0.34533563686625,0.2790253,0.48100639,"text_only_direct_or_nearby","Christian Democratic"
+"galtan",357,"Party for Bosnia and Herzegovina","SBiH","BA",2002,0.5879928515,0.2466491241,-0.3413437274,0.3413437274,0.24791075,0.4587310175,"both_direct_or_nearby","Unclassified"

validation/plot_workflow.R

@@ -0,0 +1,96 @@
+#!/usr/bin/env Rscript
+
+script_args <- commandArgs(trailingOnly = FALSE)
+file_arg <- script_args[grepl("^--file=", script_args)][1]
+script_path <- normalizePath(sub("^--file=", "", file_arg))
+repo_root <- normalizePath(file.path(dirname(script_path), ".."))
+setwd(repo_root)
+
+dir.create(file.path("revision", "figures"), recursive = TRUE, showWarnings = FALSE)
+
+library(grid)
+
+# Draw an overarching section container with a title header
+draw_section <- function(x, y, width, height, title, bg = "#FAFAFA", border = "#333333") {
+  grid.roundrect(x = unit(x, "npc"), y = unit(y, "npc"),
+                 width = unit(width, "npc"), height = unit(height, "npc"),
+                 r = unit(.018, "snpc"),
+                 gp = gpar(fill = bg, col = border, lwd = .9))
+  
+  # Header bar
+  header_h <- .040
+  grid.roundrect(x = unit(x, "npc"), y = unit(y + height/2 - header_h/2, "npc"),
+                 width = unit(width, "npc"), height = unit(header_h, "npc"),
+                 r = unit(.018, "snpc"),
+                 gp = gpar(fill = "#EBEBEB", col = border, lwd = .9))
+  grid.text(title, x = unit(x - width*0.46, "npc"), y = unit(y + height/2 - header_h/2, "npc"),
+            just = "left", gp = gpar(fontsize = 9, fontface = "bold", col = "#111111"))
+}
+
+# Sub-box
+draw_box <- function(x, y, width, height, title, text_items = c(), bg = "#FFFFFF", border = "#555555") {
+  grid.roundrect(x = unit(x, "npc"), y = unit(y, "npc"),
+                 width = unit(width, "npc"), height = unit(height, "npc"),
+                 r = unit(.015, "snpc"),
+                 gp = gpar(fill = bg, col = border, lwd = .75))
+  grid.text(title, x = unit(x, "npc"), y = unit(y + height*0.28, "npc"),
+            gp = gpar(fontsize = 8.2, fontface = "bold", col = "#111111"))
+  
+  if (length(text_items) > 0) {
+    txt <- paste(text_items, collapse = "\n")
+    grid.text(txt, x = unit(x, "npc"), y = unit(y - height*0.18, "npc"),
+              gp = gpar(fontsize = 7.4, fontface = "plain", col = "#333333"))
+  }
+}
+
+arrow <- function(x0, y0, x1, y1) {
+  grid.lines(x = unit(c(x0, x1), "npc"), y = unit(c(y0, y1), "npc"),
+             arrow = grid::arrow(length = unit(.055, "inches"), type = "closed"),
+             gp = gpar(col = "#222222", fill = "#222222", lwd = .9))
+}
+
+cairo_pdf(file.path("revision", "figures", "research_workflow.pdf"),
+          width = 8.2, height = 7.6, onefile = TRUE)
+grid.newpage()
+
+# ===========================================================================
+# Area 1: Data Acquisition
+# ===========================================================================
+draw_section(.5, .865, .94, .21, "1. Data Acquisition")
+draw_box(.27, .84, .42, .12, "Text Data Projects", c("• Manifesto platforms", "• Campaign media text"))
+draw_box(.73, .84, .42, .12, "Expert Survey Projects", c("• CHES, V-Party, POPPA & GPS", "• Morgan historical survey"))
+
+arrow(.5, .76, .5, .72)
+
+# ===========================================================================
+# Area 2: Data Harmonization
+# ===========================================================================
+draw_section(.5, .62, .94, .18, "2. Data Harmonization")
+draw_box(.20, .595, .30, .10, "Party Identification", c("• PartyFacts harmonization", "• Alliance mapping"))
+draw_box(.50, .595, .26, .10, "Item Orientation", c("• Directional polarity", "• Inverted item reversal"))
+draw_box(.80, .595, .28, .10, "Scale Harmonization", c("• Text item counts", "• Expert scale mapping"))
+
+arrow(.5, .53, .5, .49)
+
+# ===========================================================================
+# Area 3: Dynamic Two-Dimensional Modeling
+# ===========================================================================
+draw_section(.5, .385, .94, .19, "3. Dynamic Two-Dimensional Modeling", bg = "#F4F4F4", border = "#111111")
+draw_box(.20, .36, .30, .11, "Latent State Process", c("• Economic & Cultural scales", "• Segment random walks"))
+draw_box(.50, .36, .26, .11, "Likelihood Functions", c("• Hurdle-binomial text counts", "• Beta-binomial expert means"))
+draw_box(.80, .36, .28, .11, "Estimation & Anchoring", c("• General left-right mixtures", "• Hamiltonian Monte Carlo"))
+
+arrow(.5, .29, .5, .25)
+
+# ===========================================================================
+# Area 4: Data Extraction & Validation
+# ===========================================================================
+draw_section(.5, .135, .94, .20, "4. Data Extraction & Validation")
+draw_box(.20, .11, .30, .12, "Primary Panel Release", c("• 1944–2025 election panel", "• Posterior means & CIs"))
+draw_box(.50, .11, .26, .12, "Secondary Output", c("• Annual trajectories", "• Composition metadata"))
+draw_box(.80, .11, .28, .12, "Validation & Diagnostics", c("• Blocked holdout & PPC", "• Ablation & sensitivity"))
+
+dev.off()
+
+message("Wrote validation/figures/research_workflow.pdf")
+

validation/prepare_blocked_validation.jl

@@ -0,0 +1,143 @@
+#!/usr/bin/env julia
+
+using CSV, DataFrames, Random, Dates, SHA
+
+const SEED = 20260812
+const HOLDOUT_SHARE = 0.20
+
+function region_for(country::AbstractString)
+    europe = Set(["AL","AT","BA","BE","BG","BY","CH","CY","CZ","DE","DK","EE","ES","FI","FR","GB","GE","GR","HR","HU","IE","IS","IT","LT","LU","LV","MD","ME","MK","MT","NL","NO","PL","PT","RO","RS","RU","SE","SI","SK","TR","UA"])
+    latin = Set(["AR","BO","BR","CL","CO","CR","DO","EC","MX","PA","PE","UY"])
+    north_america = Set(["CA", "US"])
+    asia_pacific = Set(["AU", "JP", "KR", "LK", "NZ"])
+    country in europe && return "Europe"
+    country in latin && return "Latin America"
+    country in north_america && return "North America"
+    country in asia_pacific && return "Asia-Pacific"
+    return "Other"
+end
+
+function sha256_file(path::String)
+    open(path) do io
+        return bytes2hex(sha256(io))
+    end
+end
+
+function main(args=ARGS)
+    repo_root = normpath(joinpath(@__DIR__, ".."))
+    output_dir = isempty(args) ? joinpath(repo_root, "_local", "revision", "blocked_party") : abspath(args[1])
+    mkpath(output_dir)
+    mkpath(joinpath(output_dir, "data"))
+
+    text_path = joinpath(repo_root, "data", "text_data.csv")
+    expert_path = joinpath(repo_root, "data", "expert.csv")
+    lr_path = joinpath(repo_root, "data", "lr_data.csv")
+    union_path = joinpath(repo_root, "data", "union_mapping.csv")
+
+    text = CSV.read(text_path, DataFrame)
+    expert = CSV.read(expert_path, DataFrame)
+    lr = CSV.read(lr_path, DataFrame)
+    unions = CSV.read(union_path, DataFrame)
+
+    union_ids = Set(vcat(Int.(unions.manifesto_pf_id), Int.(unions.expert_pf_id)))
+    protected_ids = union(union_ids, Set([1375])) # preserve CDU identification anchor and avoid union leakage
+
+    text_parties = Set(Int.(unique(text.party)))
+    expert_parties = Set(Int.(unique(expert.party)))
+    candidate_ids = sort(collect(intersect(text_parties, expert_parties)))
+
+    candidate_rows = NamedTuple[]
+    for party in candidate_ids
+        party in protected_ids && continue
+        t = text[text.party .== party, :]
+        e = expert[expert.party .== party, :]
+        isempty(e.year) && continue
+        text_years = length(unique(t.year))
+        text_years < 3 && continue
+        has_economic_text = any((t.type_high .== "pro_market") .| (t.type_high .== "pro_welfare"))
+        has_cultural_text = any((t.type_high .== "cosmopolitan") .| (t.type_high .== "traditional"))
+        (has_economic_text && has_cultural_text) || continue
+        country = string(first(e.country))
+        first_expert_year = minimum(e.year)
+        period = first_expert_year < 1990 ? "pre-1990" :
+                 first_expert_year < 2000 ? "1990s" :
+                 first_expert_year < 2010 ? "2000s" :
+                 first_expert_year < 2020 ? "2010s" : "2020s"
+        push!(candidate_rows, (
+            party_id=party,
+            country=country,
+            region=region_for(country),
+            first_expert_year=first_expert_year,
+            last_expert_year=maximum(e.year),
+            expert_period=period,
+            text_years=text_years,
+            expert_rows=nrow(e),
+            lr_rows=count(lr.party .== party)
+        ))
+    end
+    candidates = DataFrame(candidate_rows)
+    candidates.stratum = candidates.region .* " / " .* candidates.expert_period
+
+    rng = MersenneTwister(SEED)
+    selected_ids = Int[]
+    for stratum in sort(unique(candidates.stratum))
+        ids = sort(candidates.party_id[candidates.stratum .== stratum])
+        shuffle!(rng, ids)
+        n_select = max(1, round(Int, HOLDOUT_SHARE * length(ids)))
+        n_select = min(n_select, length(ids))
+        append!(selected_ids, ids[1:n_select])
+    end
+    sort!(unique!(selected_ids))
+    selected = candidates[in.(candidates.party_id, Ref(Set(selected_ids))), :]
+    selected.selected = trues(nrow(selected))
+
+    expert_test = expert[in.(Int.(expert.party), Ref(Set(selected_ids))), :]
+    expert_train = expert[.!in.(Int.(expert.party), Ref(Set(selected_ids))), :]
+    lr_test = lr[in.(Int.(lr.party), Ref(Set(selected_ids))), :]
+    lr_train = lr[.!in.(Int.(lr.party), Ref(Set(selected_ids))), :]
+
+    # All text remains in training, which ensures that the held-out parties are
+    # estimated from their text trajectories and the model's shared structure.
+    CSV.write(joinpath(output_dir, "text_data.csv"), text)
+    CSV.write(joinpath(output_dir, "expert.csv"), expert_train)
+    CSV.write(joinpath(output_dir, "lr_data.csv"), lr_train)
+    CSV.write(joinpath(output_dir, "expert_test.csv"), expert_test)
+    CSV.write(joinpath(output_dir, "lr_data_test.csv"), lr_test)
+    CSV.write(joinpath(output_dir, "blocked_parties.csv"), selected)
+    cp(union_path, joinpath(output_dir, "data", "union_mapping.csv"), force=true)
+
+    # Assertions against leakage and accidental loss of the text evidence.
+    @assert isempty(intersect(Set(Int.(expert_train.party)), Set(selected_ids)))
+    @assert isempty(intersect(Set(Int.(lr_train.party)), Set(selected_ids)))
+    @assert Set(selected_ids) ⊆ Set(Int.(text.party))
+    @assert nrow(expert_train) + nrow(expert_test) == nrow(expert)
+    @assert nrow(lr_train) + nrow(lr_test) == nrow(lr)
+
+    manifest = DataFrame(
+        field = [
+            "created_at", "seed", "holdout_share", "eligible_parties", "blocked_parties",
+            "text_rows_train", "expert_rows_train", "expert_rows_test", "lr_rows_train", "lr_rows_test",
+            "text_sha256", "expert_full_sha256", "lr_full_sha256", "union_mapping_sha256"
+        ],
+        value = [
+            string(now()), string(SEED), string(HOLDOUT_SHARE), string(nrow(candidates)), string(length(selected_ids)),
+            string(nrow(text)), string(nrow(expert_train)), string(nrow(expert_test)), string(nrow(lr_train)), string(nrow(lr_test)),
+            sha256_file(text_path), sha256_file(expert_path), sha256_file(lr_path), sha256_file(union_path)
+        ]
+    )
+    CSV.write(joinpath(output_dir, "blocked_validation_manifest.csv"), manifest)
+
+    stratum_summary = combine(groupby(candidates, :stratum),
+        nrow => :eligible_parties,
+        :party_id => (x -> count(in(Set(selected_ids)), x)) => :blocked_parties)
+    CSV.write(joinpath(output_dir, "blocked_validation_strata.csv"), stratum_summary)
+
+    println("Prepared blocked validation at: $output_dir")
+    println("Eligible parties: $(nrow(candidates))")
+    println("Blocked parties: $(length(selected_ids))")
+    println("Held-out dimension-specific expert rows: $(nrow(expert_test))")
+    println("Held-out general left-right rows: $(nrow(lr_test))")
+    println("No selected party remains in either expert training file.")
+end
+
+main()

validation/run_blocked_validation.sh

@@ -0,0 +1,76 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+repo_root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd -P)"
+cd "$repo_root"
+
+if [[ "${PARTY2D_APPROVE_MODEL_FIT:-}" != "YES" ]]; then
+  echo "Refusing to start a Stan fit without explicit approval." >&2
+  echo "After approval, rerun with PARTY2D_APPROVE_MODEL_FIT=YES." >&2
+  exit 64
+fi
+
+run_dir="${1:-$repo_root/_local/validation/blocked_party}"
+mkdir -p "$run_dir" "$repo_root/_local/tmp/blocked_validation"
+
+if pgrep -af 'stan_model_2dim_v6.*sample|src/julia/01_run_model.jl' | grep -v "$$" >/dev/null; then
+  echo "Refusing to start: another party2d model process appears to be running." >&2
+  pgrep -af 'stan_model_2dim_v6.*sample|src/julia/01_run_model.jl' >&2 || true
+  exit 1
+fi
+
+if [[ -f "$run_dir/model_fit.complete" ]]; then
+  echo "Blocked validation already marked complete: $run_dir/model_fit.complete" >&2
+  exit 1
+fi
+
+required_kb=$((80 * 1024 * 1024))
+available_kb="$(df -Pk "$run_dir" | awk 'NR==2 {print $4}')"
+if (( available_kb < required_kb )); then
+  echo "Refusing to start: less than 80 GiB free on the run volume." >&2
+  exit 1
+fi
+
+julia --project=. validation/prepare_blocked_validation.jl "$run_dir"
+sha256sum "$run_dir"/text_data.csv "$run_dir"/expert.csv "$run_dir"/lr_data.csv \
+  "$run_dir"/expert_test.csv "$run_dir"/lr_data_test.csv \
+  > "$run_dir/input_sha256sums.txt"
+
+export CMDSTAN_HOME="${CMDSTAN_HOME:-/opt/agent-tools/cmdstan-2.39.0}"
+export TMPDIR="$repo_root/_local/tmp/blocked_validation"
+export JULIA_NUM_THREADS="${JULIA_NUM_THREADS:-4}"
+export STAN_NUM_THREADS=1
+export STAN_REFRESH="${STAN_REFRESH:-50}"
+export PARTY2D_NUM_CHAINS="${PARTY2D_NUM_CHAINS:-4}"
+export PARTY2D_NUM_WARMUP="${PARTY2D_NUM_WARMUP:-1000}"
+export PARTY2D_NUM_SAMPLES="${PARTY2D_NUM_SAMPLES:-1000}"
+
+if [[ ! -x "$CMDSTAN_HOME/bin/stanc" ]]; then
+  echo "Refusing to start: no executable stanc compiler at $CMDSTAN_HOME/bin/stanc" >&2
+  exit 1
+fi
+"$CMDSTAN_HOME/bin/stanc" --version > "$run_dir/stanc_version.txt"
+
+date --iso-8601=seconds > "$run_dir/model_fit.started"
+echo "$$" > "$run_dir/model_fit.pid"
+echo "CMDSTAN_HOME=$CMDSTAN_HOME" > "$run_dir/model_fit.environment"
+echo "JULIA_NUM_THREADS=$JULIA_NUM_THREADS" >> "$run_dir/model_fit.environment"
+echo "STAN_NUM_THREADS=$STAN_NUM_THREADS" >> "$run_dir/model_fit.environment"
+echo "PARTY2D_NUM_CHAINS=$PARTY2D_NUM_CHAINS" >> "$run_dir/model_fit.environment"
+echo "PARTY2D_NUM_WARMUP=$PARTY2D_NUM_WARMUP" >> "$run_dir/model_fit.environment"
+echo "PARTY2D_NUM_SAMPLES=$PARTY2D_NUM_SAMPLES" >> "$run_dir/model_fit.environment"
+
+set +e
+nice -n 10 julia --project=. src/julia/01_run_model.jl --data-dir "$run_dir" \
+  2>&1 | tee "$run_dir/model_fit.log"
+status="${PIPESTATUS[0]}"
+set -e
+
+echo "$status" > "$run_dir/model_fit.exit_code"
+if [[ "$status" -eq 0 ]]; then
+  rm -f "$run_dir/model_fit.failed"
+  date --iso-8601=seconds > "$run_dir/model_fit.complete"
+else
+  date --iso-8601=seconds > "$run_dir/model_fit.failed"
+fi
+exit "$status"

validation/run_postprocessing.R

@@ -0,0 +1,464 @@
+#!/usr/bin/env Rscript
+
+options(stringsAsFactors = FALSE)
+
+required_packages <- c("ggplot2", "sandwich", "lme4")
+missing_packages <- required_packages[!vapply(required_packages, requireNamespace, logical(1), quietly = TRUE)]
+if (length(missing_packages)) stop("Missing R packages: ", paste(missing_packages, collapse = ", "))
+
+library(ggplot2)
+
+script_args <- commandArgs(trailingOnly = FALSE)
+file_arg <- script_args[grepl("^--file=", script_args)][1]
+script_path <- normalizePath(sub("^--file=", "", file_arg))
+repo_root <- normalizePath(file.path(dirname(script_path), ".."))
+setwd(repo_root)
+
+output_dir <- file.path("revision", "outputs")
+figure_dir <- file.path("revision", "figures")
+dir.create(output_dir, recursive = TRUE, showWarnings = FALSE)
+dir.create(figure_dir, recursive = TRUE, showWarnings = FALSE)
+
+panel_file <- file.path("data", "releases", "party_2d_election_year_panel_v0.csv.gz")
+annual_file <- file.path("data", "releases", "party_2d_annual_model_output_v0.csv.gz")
+no_vparty_file <- "/projects/party4d_validation_runs/no_vparty/outputs/estimations/latest/party_positions_2026-06-05_14-57-17.csv"
+
+for (f in c(panel_file, annual_file, no_vparty_file)) {
+  if (!file.exists(f)) stop("Required input not found: ", f)
+}
+
+panel <- read.csv(panel_file, check.names = FALSE)
+annual <- read.csv(annual_file, check.names = FALSE)
+no_vparty <- read.csv(no_vparty_file, check.names = FALSE)
+families <- read.csv(file.path("data", "party_families.csv"), check.names = FALSE)
+names(families)[names(families) == "partyfacts_id"] <- "party_id"
+
+iso_names <- c(
+  AL="Albania", AM="Armenia", AR="Argentina", AT="Austria", AU="Australia",
+  AZ="Azerbaijan", BA="Bosnia and Herzegovina", BE="Belgium", BG="Bulgaria",
+  BO="Bolivia", BR="Brazil", BY="Belarus", CA="Canada", CH="Switzerland",
+  CL="Chile", CO="Colombia", CR="Costa Rica", CY="Cyprus", CZ="Czechia",
+  DE="Germany", DK="Denmark", DO="Dominican Republic", EC="Ecuador",
+  EE="Estonia", ES="Spain", FI="Finland", FR="France", GB="United Kingdom",
+  GE="Georgia", GR="Greece", HR="Croatia", HU="Hungary", IE="Ireland",
+  IL="Israel", IS="Iceland", IT="Italy", JP="Japan", KR="South Korea",
+  LK="Sri Lanka", LT="Lithuania", LU="Luxembourg", LV="Latvia", MD="Moldova",
+  ME="Montenegro", MK="North Macedonia", MT="Malta", MX="Mexico",
+  NL="Netherlands", NO="Norway", NZ="New Zealand", PA="Panama", PE="Peru",
+  PL="Poland", PT="Portugal", RO="Romania", RS="Serbia", RU="Russia",
+  SE="Sweden", SI="Slovenia", SK="Slovakia", TR="Türkiye", UA="Ukraine",
+  US="United States", UY="Uruguay", ZA="South Africa"
+)
+
+region_for <- function(country) {
+  europe <- c("AL","AT","BA","BE","BG","BY","CH","CY","CZ","DE","DK","EE","ES","FI","FR","GB","GE","GR","HR","HU","IE","IS","IT","LT","LU","LV","MD","ME","MK","MT","NL","NO","PL","PT","RO","RS","RU","SE","SI","SK","TR","UA")
+  latin <- c("AR","BO","BR","CL","CO","CR","DO","EC","MX","PA","PE","UY")
+  north_america <- c("CA", "US")
+  asia_pacific <- c("AU", "JP", "KR", "LK", "NZ")
+  out <- rep("Other", length(country))
+  out[country %in% europe] <- "Europe"
+  out[country %in% latin] <- "Latin America"
+  out[country %in% north_america] <- "North America"
+  out[country %in% asia_pacific] <- "Asia-Pacific"
+  out
+}
+
+family_names <- c(
+  com="Communist/Far Left", eco="Green/Ecological", soc="Social Democratic",
+  chr="Christian Democratic", agr="Agrarian", lib="Liberal", con="Conservative",
+  right="Radical Right", spec="Special issue", other="Other", nofam="Unclassified"
+)
+
+theme_revision <- function() {
+  theme_minimal(base_size = 9) +
+    theme(
+      panel.grid.minor = element_blank(),
+      strip.text = element_text(face = "bold"),
+      legend.position = "bottom",
+      plot.title.position = "plot"
+    )
+}
+
+write_clean_csv <- function(x, path) {
+  write.csv(x, path, row.names = FALSE, na = "")
+  message("Wrote ", path, " (", nrow(x), " rows)")
+}
+
+# ---------------------------------------------------------------------------
+# Versioned country coverage
+# ---------------------------------------------------------------------------
+
+country_rows <- lapply(sort(unique(panel$country)), function(cc) {
+  d <- panel[panel$country == cc, ]
+  tab <- table(d$source_support_class)
+  get_n <- function(label) if (label %in% names(tab)) unname(tab[label]) else 0
+  data.frame(
+    release = "v0", iso2 = cc, country = unname(iso_names[cc]),
+    first_year = min(d$year), last_year = max(d$year),
+    parties = length(unique(d$party_id)), election_year_rows = nrow(d),
+    both_text_expert = get_n("both_direct_or_nearby"),
+    text_only = get_n("text_only_direct_or_nearby"),
+    expert_only = get_n("expert_only_direct_or_nearby"),
+    temporal_propagation = get_n("temporal_propagation")
+  )
+})
+country_coverage <- do.call(rbind, country_rows)
+write_clean_csv(country_coverage, file.path("metadata", "country_coverage_v0.csv"))
+
+# ---------------------------------------------------------------------------
+# Source-support balance with uncertainty and hierarchical heterogeneity
+# ---------------------------------------------------------------------------
+
+balance <- panel[is.na(panel$pervote) | panel$pervote > 0, ]
+balance$pervote[is.na(balance$pervote)] <- 0
+balance$decade <- factor(floor(balance$year / 10) * 10)
+balance$region <- factor(region_for(balance$country))
+balance$country <- factor(balance$country)
+balance$text_only <- as.integer(balance$source_support_class == "text_only_direct_or_nearby")
+balance$expert_only <- as.integer(balance$source_support_class == "expert_only_direct_or_nearby")
+balance$temporal <- as.integer(balance$source_support_class == "temporal_propagation")
+
+class_labels <- c(
+  text_only="Text only", expert_only="Expert only", temporal="Temporal propagation"
+)
+
+pooled_rows <- list()
+interaction_rows <- list()
+row_i <- 1
+interaction_i <- 1
+
+for (dimension in c("economic_lr", "galtan")) {
+  form <- as.formula(paste(dimension, "~ text_only + expert_only + temporal + factor(country) + decade + log1p(pervote)"))
+  model <- lm(form, data = balance)
+  cluster_vcov <- sandwich::vcovCL(model, cluster = balance$country, type = "HC1")
+  for (term in names(class_labels)) {
+    estimate <- unname(coef(model)[term])
+    se <- sqrt(cluster_vcov[term, term])
+    category <- switch(term,
+      text_only="text_only_direct_or_nearby",
+      expert_only="expert_only_direct_or_nearby",
+      temporal="temporal_propagation")
+    pooled_rows[[row_i]] <- data.frame(
+      dimension = dimension, comparison = class_labels[term],
+      category_n = sum(balance$source_support_class == category),
+      model_n = nobs(model), estimate = estimate, clustered_se = se,
+      ci_lower = estimate - 1.96 * se, ci_upper = estimate + 1.96 * se,
+      p_value = 2 * pnorm(abs(estimate / se), lower.tail = FALSE)
+    )
+    row_i <- row_i + 1
+  }
+
+  # Partially pool the well-populated text-only contrast across region and
+  # decade. Expert-only and temporal-propagation contrasts remain pooled
+  # because their samples are too sparse for stable varying slopes.
+  varying_form <- as.formula(paste(
+    dimension,
+    "~ expert_only + temporal + text_only + log1p(pervote) +",
+    "(1 | country) + (0 + text_only | region) + (0 + text_only | decade)"
+  ))
+  varying_model <- lme4::lmer(varying_form, data = balance, REML = TRUE,
+                              control = lme4::lmerControl(check.nobs.vs.nRE = "ignore"))
+  fixed_text <- lme4::fixef(varying_model)["text_only"]
+  fixed_var <- as.matrix(vcov(varying_model))["text_only", "text_only"]
+  re <- lme4::ranef(varying_model, condVar = TRUE)
+
+  for (group_type in c("region", "decade")) {
+    group_values <- rownames(re[[group_type]])
+    post_var <- attr(re[[group_type]], "postVar")
+    for (group_index in seq_along(group_values)) {
+      g <- group_values[group_index]
+      template <- balance[as.character(balance[[group_type]]) == g, , drop = FALSE]
+      estimate <- fixed_text + re[[group_type]][g, "text_only"]
+      # Approximate conditional interval. Adding fixed-effect and conditional
+      # random-effect variances is conservative because their covariance is
+      # not exposed by ranef().
+      se <- sqrt(fixed_var + post_var[1, 1, group_index])
+      interaction_rows[[interaction_i]] <- data.frame(
+        dimension = dimension, group_type = group_type, group = as.character(g),
+        comparison = "Text only", group_n = nrow(template),
+        text_only_n = sum(template$source_support_class == "text_only_direct_or_nearby"),
+        estimate = unname(estimate), approximate_se = unname(se),
+        ci_lower = unname(estimate - 1.96 * se), ci_upper = unname(estimate + 1.96 * se),
+        model_n = nobs(varying_model), singular_fit = lme4::isSingular(varying_model)
+      )
+      interaction_i <- interaction_i + 1
+    }
+  }
+}
+
+pooled <- do.call(rbind, pooled_rows)
+interactions <- do.call(rbind, interaction_rows)
+write_clean_csv(pooled, file.path(output_dir, "source_support_pooled.csv"))
+write_clean_csv(interactions, file.path(output_dir, "source_support_interactions.csv"))
+
+interactions$dimension_label <- ifelse(interactions$dimension == "economic_lr", "Economic", "Cultural")
+interactions$group_type_label <- ifelse(interactions$group_type == "region", "Region", "Decade")
+p_balance <- ggplot(interactions, aes(x = estimate, y = reorder(group, estimate))) +
+  geom_vline(xintercept = 0, colour = "grey40", linewidth = 0.4, linetype = 2) +
+  geom_errorbar(aes(xmin = ci_lower, xmax = ci_upper), orientation = "y", width = 0, linewidth = 0.4, colour = "grey20") +
+  geom_point(size = 1.6, colour = "black") +
+  facet_wrap(~dimension_label + group_type_label, scales = "free_y", ncol = 2) +
+  labs(x = "Adjusted difference from overlapping text-and-expert support", y = NULL) +
+  theme_revision()
+ggsave(file.path(figure_dir, "source_support_heterogeneity.pdf"), p_balance,
+       width = 8.2, height = 6.8, device = cairo_pdf)
+
+# ---------------------------------------------------------------------------
+# V-Party ablation: case and subgroup sensitivity
+# ---------------------------------------------------------------------------
+
+keys <- c("party_id", "country", "year")
+vparty <- merge(panel, no_vparty, by = keys, suffixes = c("_production", "_no_vparty"))
+vparty <- merge(vparty, families, by = "party_id", all.x = TRUE)
+vparty$family[is.na(vparty$family)] <- "nofam"
+vparty$family_label <- unname(family_names[vparty$family])
+vparty$region <- region_for(vparty$country)
+vparty$decade <- floor(vparty$year / 10) * 10
+
+for (dimension in c("economic_lr", "galtan")) {
+  prod <- vparty[[paste0(dimension, "_production")]]
+  ablated <- vparty[[paste0(dimension, "_no_vparty")]]
+  vparty[[paste0(dimension, "_difference")]] <- ablated - prod
+  vparty[[paste0(dimension, "_abs_difference")]] <- abs(ablated - prod)
+  width_prod <- vparty[[paste0(dimension, "_q975_production")]] - vparty[[paste0(dimension, "_q025_production")]]
+  width_ablation <- vparty[[paste0(dimension, "_q975_no_vparty")]] - vparty[[paste0(dimension, "_q025_no_vparty")]]
+  vparty[[paste0(dimension, "_interval_width_production")]] <- width_prod
+  vparty[[paste0(dimension, "_interval_width_no_vparty")]] <- width_ablation
+  vparty[[paste0(dimension, "_interval_width_change")]] <- width_ablation - width_prod
+}
+
+summary_by <- function(data, group_var, dimension) {
+  split_data <- split(data, data[[group_var]], drop = TRUE)
+  rows <- lapply(names(split_data), function(g) {
+    d <- split_data[[g]]
+    absdiff <- d[[paste0(dimension, "_abs_difference")]]
+    diff <- d[[paste0(dimension, "_difference")]]
+    wprod <- d[[paste0(dimension, "_interval_width_production")]]
+    wabl <- d[[paste0(dimension, "_interval_width_no_vparty")]]
+    data.frame(
+      dimension = dimension, group_type = group_var, group = g,
+      n = nrow(d), parties = length(unique(d$party_id)),
+      mean_abs_difference = mean(absdiff), median_abs_difference = median(absdiff),
+      p90_abs_difference = unname(quantile(absdiff, .90)),
+      p95_abs_difference = unname(quantile(absdiff, .95)),
+      mean_signed_difference = mean(diff),
+      mean_interval_width_production = mean(wprod),
+      mean_interval_width_no_vparty = mean(wabl),
+      mean_interval_width_change = mean(wabl - wprod)
+    )
+  })
+  do.call(rbind, rows)
+}
+
+vparty_summaries <- do.call(rbind, lapply(c("country", "decade", "region", "family_label", "source_support_class"), function(g) {
+  do.call(rbind, lapply(c("economic_lr", "galtan"), function(d) summary_by(vparty, g, d)))
+}))
+
+write_clean_csv(vparty, file.path(output_dir, "vparty_sensitivity_matched_rows.csv"))
+write_clean_csv(vparty_summaries, file.path(output_dir, "vparty_sensitivity_groups.csv"))
+
+top_cases <- do.call(rbind, lapply(c("economic_lr", "galtan"), function(dimension) {
+  ord <- order(vparty[[paste0(dimension, "_abs_difference")]], decreasing = TRUE)
+  d <- vparty[head(ord, 30), ]
+  data.frame(
+    dimension = dimension, party_id = d$party_id,
+    party_name = d$party_name_english, party_short = d$party_name_short,
+    country = d$country, year = d$year,
+    production = d[[paste0(dimension, "_production")]],
+    no_vparty = d[[paste0(dimension, "_no_vparty")]],
+    signed_difference = d[[paste0(dimension, "_difference")]],
+    absolute_difference = d[[paste0(dimension, "_abs_difference")]],
+    production_interval_width = d[[paste0(dimension, "_interval_width_production")]],
+    no_vparty_interval_width = d[[paste0(dimension, "_interval_width_no_vparty")]],
+    source_support_class = d$source_support_class,
+    family = d$family_label
+  )
+}))
+write_clean_csv(top_cases, file.path(output_dir, "vparty_sensitivity_top_cases.csv"))
+
+country_plot <- vparty_summaries[vparty_summaries$group_type == "country" & vparty_summaries$n >= 10, ]
+country_plot <- do.call(rbind, lapply(split(country_plot, country_plot$dimension), function(d) {
+  head(d[order(d$mean_abs_difference, decreasing = TRUE), ], 15)
+}))
+country_plot$dimension_label <- ifelse(country_plot$dimension == "economic_lr", "Economic", "Cultural")
+p_vparty <- ggplot(country_plot, aes(x = mean_abs_difference, y = reorder(group, mean_abs_difference))) +
+  geom_point(aes(size = n), colour = "black") +
+  facet_wrap(~dimension_label, scales = "free_y") +
+  labs(x = "Mean absolute change after removing V-Party", y = "Country", size = "Party-years") +
+  theme_revision()
+ggsave(file.path(figure_dir, "vparty_sensitivity_countries.pdf"), p_vparty,
+       width = 7.2, height = 5.8, device = cairo_pdf)
+
+# ---------------------------------------------------------------------------
+# Illustrative trajectories and landmark cases
+# ---------------------------------------------------------------------------
+
+trajectory_ids <- c(383, 1567, 379, 409)
+traj <- panel[panel$party_id %in% trajectory_ids, ]
+traj$party_label <- factor(traj$party_id, levels = trajectory_ids,
+  labels = c("Germany: SPD", "United Kingdom: Conservatives", "Denmark: Social Democrats", "Sweden: Sweden Democrats"))
+traj_long <- rbind(
+  data.frame(traj[c("party_id","party_label","country","year","source_support_class")],
+             dimension="Economic", estimate=traj$economic_lr,
+             lower=traj$economic_lr_q025, upper=traj$economic_lr_q975),
+  data.frame(traj[c("party_id","party_label","country","year","source_support_class")],
+             dimension="Cultural", estimate=traj$galtan,
+             lower=traj$galtan_q025, upper=traj$galtan_q975)
+)
+write_clean_csv(traj_long, file.path(output_dir, "party_trajectory_plot_data.csv"))
+p_traj <- ggplot(traj_long, aes(x = year, y = estimate)) +
+  geom_ribbon(aes(ymin = lower, ymax = upper), fill = "grey85", alpha = 0.5) +
+  geom_line(colour = "black", linewidth = .65) +
+  geom_point(colour = "black", size = .9) +
+  facet_grid(dimension ~ party_label) +
+  scale_y_continuous(limits = c(0, 1), breaks = c(0, .5, 1)) +
+  labs(x = "Election year", y = "Posterior position (0–1)") +
+  theme_revision() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+ggsave(file.path(figure_dir, "party_trajectories.pdf"), p_traj,
+       width = 10.2, height = 4.9, device = cairo_pdf)
+
+landmark_spec <- data.frame(
+  party_id = c(383,383,1375,1375,1516,1516,1567,1567,487,487,409,409,433,433,1545,432,809),
+  target_year = c(1972,2021,1983,2021,1983,1997,1979,2019,1994,2022,2010,2022,1988,2022,2021,2020,2020),
+  label = c("SPD 1972","SPD 2021","CDU 1983","CDU 2021","Labour 1983","Labour 1997",
+            "Conservatives 1979","Conservatives 2019","Swedish SAP 1994","Swedish SAP 2022",
+            "Sweden Democrats 2010","Sweden Democrats 2022","French FN 1988","French FN 2022",
+            "The Left 2021","US Democrats 2020","US Republicans 2020")
+)
+
+landmark_rows <- lapply(seq_len(nrow(landmark_spec)), function(i) {
+  spec <- landmark_spec[i, ]
+  d <- panel[panel$party_id == spec$party_id & abs(panel$year - spec$target_year) <= 3, ]
+  if (!nrow(d)) return(data.frame(spec, status="missing", actual_year=NA, economic_lr=NA, galtan=NA))
+  d <- d[which.min(abs(d$year - spec$target_year)), ]
+  data.frame(spec, status=ifelse(d$year == spec$target_year, "exact", "nearest within 3 years"),
+             actual_year=d$year, party_name=d$party_name_english,
+             country=d$country, economic_lr=d$economic_lr, economic_lower=d$economic_lr_q025,
+             economic_upper=d$economic_lr_q975, galtan=d$galtan,
+             galtan_lower=d$galtan_q025, galtan_upper=d$galtan_q975,
+             source_support_class=d$source_support_class)
+})
+landmarks <- do.call(rbind, landmark_rows)
+landmarks$era <- ifelse(landmarks$target_year < 2000, "Historical & Cold War Era (1970–1999)", "Contemporary Era (2000–2022)")
+landmarks$era <- factor(landmarks$era, levels = c("Historical & Cold War Era (1970–1999)", "Contemporary Era (2000–2022)"))
+
+write_clean_csv(landmarks, file.path(output_dir, "party_landmark_plot_data.csv"))
+plot_landmarks <- landmarks[!is.na(landmarks$economic_lr), ]
+
+label_offsets <- data.frame(
+  label = landmark_spec$label,
+  dx = c(-.060,-.060,-.055,.045,-.040,-.060,.035,-.055,-.005,.080,-.060,.060,.030,-.030,.035,.080,.025),
+  dy = c(.070,.040,.055,-.035,-.070,-.040,-.025,.030,.040,.060,-.035,.035,.035,.050,.035,-.060,.035)
+)
+plot_landmarks <- merge(plot_landmarks, label_offsets, by = "label", all.x = TRUE, sort = FALSE)
+plot_landmarks$label_x <- pmin(.98, pmax(.02, plot_landmarks$economic_lr + plot_landmarks$dx))
+plot_landmarks$label_y <- pmin(.98, pmax(.02, plot_landmarks$galtan + plot_landmarks$dy))
+
+# Country shapes for clear grayscale distinction
+country_shapes <- c(DE = 16, GB = 17, SE = 15, FR = 18, US = 8)
+
+p_land <- ggplot(plot_landmarks, aes(x = economic_lr, y = galtan)) +
+  geom_errorbar(aes(ymin = galtan_lower, ymax = galtan_upper), width = 0, alpha = .35, colour = "grey30") +
+  geom_errorbar(aes(xmin = economic_lower, xmax = economic_upper), orientation = "y", width = 0, alpha = .35, colour = "grey30") +
+  geom_segment(aes(xend = label_x, yend = label_y), linewidth = .2, colour = "grey50") +
+  geom_point(aes(shape = country), size = 2.2, fill = "black", colour = "black") +
+  geom_text(aes(x = label_x, y = label_y, label = label), size = 2.4,
+            check_overlap = TRUE, show.legend = FALSE) +
+  facet_wrap(~era, ncol = 2) +
+  scale_shape_manual(values = country_shapes) +
+  scale_x_continuous(limits = c(0,1), breaks = c(0,.25,.5,.75,1)) +
+  scale_y_continuous(limits = c(0,1), breaks = c(0,.25,.5,.75,1)) +
+  labs(x = "Economic: left (0) to right (1)",
+       y = "Cultural: cosmopolitan (0) to traditionalist (1)", shape = "Country") +
+  theme_revision()
+ggsave(file.path(figure_dir, "party_landmarks.pdf"), p_land,
+       width = 9.2, height = 5.2, device = cairo_pdf)
+
+# ---------------------------------------------------------------------------
+# Predictive-interval calibration and concentration of misses
+# ---------------------------------------------------------------------------
+
+ppc_dir <- file.path(output_dir, "ppc")
+ppc_item_file <- file.path(ppc_dir, "posterior_predictive_by_dimension_item.csv")
+ppc_calibration_file <- file.path(ppc_dir, "posterior_predictive_calibration_curve.csv")
+if (file.exists(ppc_item_file) && file.exists(ppc_calibration_file)) {
+  ppc_items <- read.csv(ppc_item_file, check.names = FALSE)
+  ppc_items$dimension <- ifelse(ppc_items$dim_idx == 1, "Economic", "Cultural")
+  ppc_items$item_label <- c(
+    gender_vparty="V-Party gender", relig_vparty="V-Party religion",
+    welf_vparty="V-Party welfare", culsup_vparty="V-Party cultural superiority",
+    immig_vparty="V-Party immigration", lgbt_vparty="V-Party LGBT",
+    lrecon_vparty="V-Party economic LR", galtan_ches="CHES GAL--TAN",
+    lrecon_ches="CHES economic LR", libcon_gps="GPS cultural",
+    lrecon_gps="GPS economic LR", lrecon_poppa="POPPA economic LR"
+  )[ppc_items$var]
+  ppc_items$item_label[is.na(ppc_items$item_label)] <- ppc_items$var[is.na(ppc_items$item_label)]
+  ppc_items$item_label <- factor(ppc_items$item_label,
+    levels = rev(ppc_items$item_label[order(ppc_items$observed_coverage)]))
+  p_ppc <- ggplot(ppc_items, aes(x = observed_coverage, y = item_label)) +
+    geom_vline(xintercept = .95, linetype = 2, colour = "grey40", linewidth = .35) +
+    geom_errorbar(aes(xmin = coverage_ci_lower, xmax = coverage_ci_upper),
+                  orientation = "y", width = 0, linewidth = .35, colour = "grey20") +
+    geom_point(aes(size = n, shape = project), colour = "black", fill = "gray30") +
+    facet_wrap(~dimension, scales = "free_y") +
+    scale_x_continuous(limits = c(.70, 1), breaks = c(.70,.80,.90,.95,1),
+                       labels = function(x) paste0(round(100*x), "%")) +
+    labs(x = "Observed coverage of nominal 95% predictive interval", y = NULL,
+         shape = "Source project", size = "Ratings") +
+    theme_revision()
+  ggsave(file.path(figure_dir, "predictive_coverage_by_item.pdf"), p_ppc,
+         width = 7.8, height = 5.4, device = cairo_pdf)
+
+  ppc_cal <- read.csv(ppc_calibration_file, check.names = FALSE)
+  ppc_cal$dimension <- ifelse(ppc_cal$dim_idx == 1, "Economic", "Cultural")
+  p_cal <- ggplot(ppc_cal, aes(x = nominal_level, y = observed_coverage, linetype = dimension, shape = dimension)) +
+    geom_abline(slope = 1, intercept = 0, linetype = 3, colour = "grey50") +
+    geom_line(linewidth = .6, colour = "black") + geom_point(size = 1.8, colour = "black") +
+    scale_x_continuous(limits = c(.45, 1), breaks = c(.5,.8,.9,.95,1),
+                       labels = function(x) paste0(round(100*x), "%")) +
+    scale_y_continuous(limits = c(.4, 1), breaks = c(.4,.5,.6,.7,.8,.9,1),
+                       labels = function(x) paste0(round(100*x), "%")) +
+    labs(x = "Nominal interval level", y = "Observed coverage", linetype = "Dimension", shape = "Dimension") +
+    coord_equal() + theme_revision()
+  ggsave(file.path(figure_dir, "predictive_calibration_curve.pdf"), p_cal,
+         width = 5.7, height = 5.0, device = cairo_pdf)
+
+
+  ppc_obs_file <- file.path(ppc_dir, "posterior_predictive_observations.csv")
+  if (file.exists(ppc_obs_file)) {
+    ppc_obs <- read.csv(ppc_obs_file, check.names = FALSE)
+    ppc_obs$covered_95 <- tolower(as.character(ppc_obs$covered_95)) == "true"
+    expert_input <- read.csv(file.path("data", "expert.csv"), check.names = FALSE)
+    key <- function(d) paste(d$party, d$country, d$year, d$var, sprintf("%.10f", d$val), sep = "|")
+    ppc_obs$n_experts <- expert_input$n_experts[match(key(ppc_obs), key(expert_input))]
+    ppc_obs$interval_width <- ppc_obs$pred_upper - ppc_obs$pred_lower
+    ppc_obs$fitted_bin <- cut(ppc_obs$pred_median,
+      breaks = c(-Inf,.10,.25,.50,.75,.90,Inf), right = FALSE,
+      labels = c("<0.10","0.10--0.25","0.25--0.50","0.50--0.75","0.75--0.90",">=0.90"))
+    ppc_obs$expert_count_bin <- cut(ppc_obs$n_experts,
+      breaks = c(-Inf,1,3,5,10,Inf), right = TRUE,
+      labels = c("1","2--3","4--5","6--10",">10"))
+    width_breaks <- unique(quantile(ppc_obs$interval_width, seq(0, 1, .25), na.rm = TRUE))
+    ppc_obs$interval_width_quartile <- cut(ppc_obs$interval_width, breaks = width_breaks,
+      include.lowest = TRUE, labels = paste0("Q", seq_len(length(width_breaks)-1)))
+
+    summarize_ppc_bins <- function(variable) {
+      pieces <- split(ppc_obs, interaction(ppc_obs$dim_idx, ppc_obs[[variable]], drop = TRUE))
+      do.call(rbind, lapply(pieces, function(d) {
+        data.frame(
+          dim_idx = d$dim_idx[1], group_type = variable,
+          group = as.character(d[[variable]][1]), n = nrow(d),
+          covered = sum(d$covered_95), observed_coverage = mean(d$covered_95),
+          mean_interval_width = mean(d$interval_width),
+          mean_fitted_value = mean(d$pred_median), mean_expert_count = mean(d$n_experts, na.rm = TRUE)
+        )
+      }))
+    }
+    ppc_residual <- do.call(rbind, lapply(
+      c("fitted_bin", "expert_count_bin", "interval_width_quartile"), summarize_ppc_bins))
+    write_clean_csv(ppc_residual, file.path(ppc_dir, "posterior_predictive_residual_patterns.csv"))
+  }
+}
+
+message("Post-processing complete.")

validation/summarize_blocked_validation.jl

@@ -0,0 +1,133 @@
+#!/usr/bin/env julia
+
+using CSV
+using DataFrames
+using Statistics
+using Printf
+
+const ECON_VARS = Set(["lrecon_ches", "lrecon_poppa", "lrecon_gps", "lrecon_vparty", "welf_vparty"])
+const CULT_VARS = Set(["galtan_ches", "libcon_gps", "immig_vparty", "lgbt_vparty",
+                       "culsup_vparty", "relig_vparty", "gender_vparty"])
+
+function region_for(country::AbstractString)
+    europe = Set(["AL","AT","BA","BE","BG","BY","CH","CY","CZ","DE","DK","EE","ES","FI","FR","GB","GE","GR","HR","HU","IE","IS","IT","LT","LU","LV","MD","ME","MK","MT","NL","NO","PL","PT","RO","RS","RU","SE","SI","SK","TR","UA"])
+    latin = Set(["AR","BO","BR","CL","CO","CR","DO","EC","MX","PA","PE","UY"])
+    country in europe && return "Europe"
+    country in latin && return "Latin America"
+    country in Set(["CA", "US"]) && return "North America"
+    country in Set(["AU", "JP", "KR", "LK", "NZ"]) && return "Asia-Pacific"
+    return "Other"
+end
+
+function wilson(covered::Int, n::Int; z=1.96)
+    n == 0 && return (missing, missing)
+    p = covered / n
+    denom = 1 + z^2 / n
+    centre = (p + z^2 / (2n)) / denom
+    half = z * sqrt(p * (1-p) / n + z^2 / (4n^2)) / denom
+    return (centre - half, centre + half)
+end
+
+function safe_cor(x, y)
+    length(x) < 3 && return missing
+    std(x) == 0 && return missing
+    std(y) == 0 && return missing
+    return cor(x, y)
+end
+
+function summarize_group(d::AbstractDataFrame, group_type::String, group::String)
+    n = nrow(d)
+    overlapping = count(d.within_latent_interval_95)
+    lo, hi = wilson(overlapping, n)
+    DataFrame(
+        dimension = first(d.dimension), group_type = group_type, group = group,
+        n = n, parties = length(unique(d.party_id)),
+        pearson_r = safe_cor(d.expert_value, d.model_value),
+        mae = mean(d.absolute_error), rmse = sqrt(mean(d.error .^ 2)),
+        bias_expert_minus_model = mean(d.error),
+        latent_interval_overlap_95 = overlapping / n,
+        latent_interval_overlap_ci_lower = lo,
+        latent_interval_overlap_ci_upper = hi,
+        mean_interval_width = mean(d.interval_upper .- d.interval_lower),
+        mean_nearest_text_distance = mean(d.nearest_text_distance)
+    )
+end
+
+function main()
+    length(ARGS) >= 1 || error("Usage: summarize_blocked_validation.jl MODEL_OUTPUT [RUN_DIR] [OUTPUT_DIR]")
+    model_file = ARGS[1]
+    run_dir = length(ARGS) >= 2 ? ARGS[2] : "_local/validation/blocked_party"
+    output_dir = length(ARGS) >= 3 ? ARGS[3] : "validation/outputs"
+    mkpath(output_dir)
+
+    model = CSV.read(model_file, DataFrame)
+    expert = CSV.read(joinpath(run_dir, "expert_test.csv"), DataFrame)
+    text = CSV.read(joinpath(run_dir, "text_data.csv"), DataFrame)
+
+    party_col = :party_id in propertynames(model) ? :party_id : :party
+    model_lookup = Dict{Tuple{Int,Int}, NamedTuple}()
+    for row in eachrow(model)
+        model_lookup[(Int(row[party_col]), Int(row.year))] = (
+            economic_lr = row.economic_lr, economic_lr_q025 = row.economic_lr_q025,
+            economic_lr_q975 = row.economic_lr_q975, galtan = row.galtan,
+            galtan_q025 = row.galtan_q025, galtan_q975 = row.galtan_q975)
+    end
+
+    text_years = Dict{Int, Vector{Int}}()
+    for d in groupby(text, :party)
+        text_years[Int(first(d.party))] = sort(unique(Int.(d.year)))
+    end
+
+    rows = NamedTuple[]
+    for row in eachrow(expert)
+        key = (Int(row.party), Int(row.year))
+        haskey(model_lookup, key) || continue
+        dimension = row.var in ECON_VARS ? "economic_lr" : row.var in CULT_VARS ? "galtan" : nothing
+        isnothing(dimension) && continue
+        m = model_lookup[key]
+        if dimension == "economic_lr"
+            value, lower, upper = m.economic_lr, m.economic_lr_q025, m.economic_lr_q975
+        else
+            value, lower, upper = m.galtan, m.galtan_q025, m.galtan_q975
+        end
+        years = get(text_years, Int(row.party), Int[])
+        isempty(years) && continue
+        distance = minimum(abs.(years .- Int(row.year)))
+        distance_class = distance == 0 ? "direct text" : distance <= 3 ? "nearby text (1--3 years)" : "distant text (4+ years)"
+        err = Float64(row.val) - value
+        push!(rows, (
+            party_id = Int(row.party), country = String(row.country), region = region_for(String(row.country)),
+            year = Int(row.year), decade = string(fld(Int(row.year), 10) * 10, "s"),
+            project = String(row.project), item = String(row.var), dimension = dimension,
+            expert_value = Float64(row.val), model_value = value,
+            interval_lower = lower, interval_upper = upper, error = err,
+            absolute_error = abs(err),
+            within_latent_interval_95 = lower <= Float64(row.val) <= upper,
+            nearest_text_distance = distance, text_distance_class = distance_class
+        ))
+    end
+    predictions = DataFrame(rows)
+    isempty(predictions) && error("No held-out expert observations matched the blocked-fit output")
+
+    summaries = DataFrame[]
+    for dimension in sort(unique(predictions.dimension))
+        d_dim = predictions[predictions.dimension .== dimension, :]
+        push!(summaries, summarize_group(d_dim, "overall", "All matched held-out ratings"))
+        for variable in [:decade, :region, :text_distance_class, :project, :item]
+            for d_group in groupby(d_dim, variable)
+                push!(summaries, summarize_group(d_group, String(variable), string(first(d_group[!, variable]))))
+            end
+        end
+    end
+    summary = vcat(summaries...)
+
+    predictions_file = joinpath(output_dir, "blocked_validation_predictions.csv")
+    summary_file = joinpath(output_dir, "blocked_validation_summary.csv")
+    CSV.write(predictions_file, predictions)
+    CSV.write(summary_file, summary)
+    println("Wrote $predictions_file ($(nrow(predictions)) rows)")
+    println("Wrote $summary_file ($(nrow(summary)) rows)")
+    println(summary[summary.group_type .== "overall", :])
+end
+
+main()