Use cultural dimension labels

README.md

@@ -97,6 +97,6 @@ Original raw source files are not redistributed. Rebuilding inputs from raw file
 The two position variables are scaled from 0 to 1:
 
 - `economic_lr`: economic left to economic right.
-- `galtan`: cosmopolitan/socially liberal to traditionalist/nationalist.
+- `galtan`: cultural cosmopolitan--traditionalist position.
 
 Column definitions are in `metadata/data_dictionary.csv`.

docs/union_mapping.md

@@ -33,7 +33,7 @@ The V4 Stan model (`stan_model_2dim_v4.stan`) uses these mappings to produce **i
 3. **Expert data constrains individuals directly.** A CHES rating of CDU in 2019 maps to `theta[dim, rr_CDU_2019]` with no averaging. This is what identifies the *difference* between constituents.
 
 4. **Identification depends on data availability:**
-   - **Periods with individual expert data** (e.g., CHES 1999-2024): Constituent estimates separate meaningfully. CSU appears more conservative on galtan than CDU, matching known ground truth.
+   - **Periods with individual expert data** (e.g., CHES 1999-2024): Constituent estimates separate meaningfully. CSU appears more traditionalist on the cultural dimension than CDU, matching known ground truth.
    - **Periods without individual expert data** (e.g., 1950s-1990s): Only the shared manifesto constrains the mean. The random walk prior pulls constituents toward similar values, so CDU ≈ CSU with wide credible intervals on the gap. The estimates gradually differentiate as expert data appears.
 
 5. **Backwards compatible.** With an empty `union_mapping.csv`, all observations have `n_const=1` and V4 reduces exactly to V3.

metadata/data_dictionary.csv

@@ -9,13 +9,13 @@ party_2d_election_year_panel_vN.csv.gz,union_party_id,Union or alliance PartyFac
 party_2d_election_year_panel_vN.csv.gz,in_union,Union membership indicator,Indicator that the row is associated with a union or alliance,boolean,0;1,0,1,,indicator,,Alliance mapping,,
 party_2d_election_year_panel_vN.csv.gz,pervote,Vote share,Vote share at the election year,numeric,,0,100,,percent,,Election metadata,,
 party_2d_election_year_panel_vN.csv.gz,economic_lr,Economic left-right posterior mean,Posterior mean of economic left-right position,numeric,,0,1,,unit interval,0=left; 1=right,Posterior draws,Mean after inverse-logit transformation,
-party_2d_election_year_panel_vN.csv.gz,galtan,Cultural posterior mean,Posterior mean of cultural position,numeric,,0,1,,unit interval,0=cosmopolitan/socially liberal; 1=traditionalist/nationalist,Posterior draws,Mean after inverse-logit transformation,
+party_2d_election_year_panel_vN.csv.gz,galtan,Cultural cosmopolitan--traditionalist posterior mean,Posterior mean of cultural cosmopolitan--traditionalist position,numeric,,0,1,,unit interval,0=cosmopolitan; 1=traditionalist,Posterior draws,Mean after inverse-logit transformation,
 party_2d_election_year_panel_vN.csv.gz,economic_lr_se,Economic posterior standard error,Posterior standard deviation for economic_lr,numeric,,0,,,unit interval,,Posterior draws,Standard deviation over posterior draws,
-party_2d_election_year_panel_vN.csv.gz,galtan_se,Cultural posterior standard error,Posterior standard deviation for galtan,numeric,,0,,,unit interval,,Posterior draws,Standard deviation over posterior draws,
+party_2d_election_year_panel_vN.csv.gz,galtan_se,Cultural posterior standard error,Posterior standard deviation for the cultural estimate,numeric,,0,,,unit interval,,Posterior draws,Standard deviation over posterior draws,
 party_2d_election_year_panel_vN.csv.gz,economic_lr_q025,Economic lower posterior interval,2.5 percent posterior quantile for economic_lr,numeric,,0,1,,unit interval,0=left; 1=right,Posterior draws,Quantile over posterior draws,
 party_2d_election_year_panel_vN.csv.gz,economic_lr_q975,Economic upper posterior interval,97.5 percent posterior quantile for economic_lr,numeric,,0,1,,unit interval,0=left; 1=right,Posterior draws,Quantile over posterior draws,
-party_2d_election_year_panel_vN.csv.gz,galtan_q025,Cultural lower posterior interval,2.5 percent posterior quantile for galtan,numeric,,0,1,,unit interval,0=cosmopolitan/socially liberal; 1=traditionalist/nationalist,Posterior draws,Quantile over posterior draws,
+party_2d_election_year_panel_vN.csv.gz,galtan_q025,Cultural lower posterior interval,2.5 percent posterior quantile for the cultural estimate,numeric,,0,1,,unit interval,0=cosmopolitan; 1=traditionalist,Posterior draws,Quantile over posterior draws,
-party_2d_election_year_panel_vN.csv.gz,galtan_q975,Cultural upper posterior interval,97.5 percent posterior quantile for galtan,numeric,,0,1,,unit interval,0=cosmopolitan/socially liberal; 1=traditionalist/nationalist,Posterior draws,Quantile over posterior draws,
+party_2d_election_year_panel_vN.csv.gz,galtan_q975,Cultural upper posterior interval,97.5 percent posterior quantile for the cultural estimate,numeric,,0,1,,unit interval,0=cosmopolitan; 1=traditionalist,Posterior draws,Quantile over posterior draws,
 party_2d_election_year_panel_vN.csv.gz,election_id,Election identifier,Election identifier where available,string,,,,,identifier,,Election metadata,,Missing where no election identifier is available
 party_2d_election_year_panel_vN.csv.gz,election_date,Election date,Election date where available,date,,,,,date,,Election metadata,,Missing in current processed election metadata
 party_2d_election_year_panel_vN.csv.gz,has_text,Text support indicator,Indicator for direct or nearby text evidence,boolean,0;1,0,1,,indicator,,Source-support construction,Nearby threshold documented in source_support_dictionary.csv,

src/julia/01_run_model.jl

@@ -5,7 +5,7 @@
 ## Executes the complete pipeline: data loading → preparation → model fitting
 ##
 ## Supports two model versions:
-##   - "2dim": 2D bipolar model (V1) - estimates economic_lr and galtan directly
+##   - "2dim": 2D bipolar model (V1) - estimates economic left-right and cultural cosmopolitan--traditionalist positions directly
 ##   - "4dim": 4D unipolar model (V10) - estimates 4 traits, derives 2 scales
 ##
 ## Default is 2D model (better identification, faster convergence)
@@ -40,7 +40,7 @@ println("  Adapt delta: $(ADAPT_DELTA)")
 println("  Max depth: $(MAX_DEPTH)")
 println("  Start year: $(START_YEAR)")
 if MODEL_VERSION == "2dim"
-    println("\n  2D MODEL: Estimates economic_lr and galtan directly")
+    println("\n  2D MODEL: Estimates economic left-right and cultural cosmopolitan--traditionalist positions directly")
     println("           (Half the parameters, better convergence)")
 else
     println("\n  4D MODEL: Estimates 4 traits, derives 2 scales")
@@ -256,7 +256,7 @@ function run_model(;
         println("  Expert dimension-specific observations: $(dat_4dim["N_exp_dim"])")
         println("  Expert general L-R observations: $(dat_4dim["N_exp_lr"])")
         if MODEL_VERSION == "2dim"
-            println("  Dimensions estimated: 2 (economic_lr, galtan)")
+            println("  Dimensions estimated: 2 (economic left-right, cultural cosmopolitan--traditionalist)")
             println("  Theta parameters: $(2 * data_prep.R) (2 × R)")
         else
             println("  Dimensions estimated: 4 (pro_market, pro_welfare, cosmopolitan, traditional)")

src/julia/02_post_estimation.jl

@@ -3,7 +3,7 @@
 02_post_estimation.jl - Extract party position estimates from Stan model output
 
 Supports both model versions:
-- 2D model (V1): Extracts economic_lr and galtan directly
+- 2D model (V1): Extracts economic left-right and cultural cosmopolitan--traditionalist positions directly
 - 4D model (V10): Extracts 4 traits + 2 derived scales
 
 V10/V1 UPDATE: Handles segment-based indexing
@@ -520,7 +520,7 @@ function extract_estimates(chains::DataFrame, segment_year_map::DataFrame, R::In
 
     # Select quantities based on model version
     if model_version == "2dim"
-        # 2D model: economic_lr and galtan are directly estimated
+        # 2D model: economic left-right and cultural cosmopolitan--traditionalist positions are directly estimated
         # (general_lr is computed in Stan for anchoring but not extracted as output)
         quantities = ["economic_lr", "galtan"]
         test_col = "economic_lr.1"
@@ -703,7 +703,7 @@ function validate_output(output::DataFrame, segment_info::Union{DataFrame, Nothi
         econ_marker = econ_ok ? "" : "*"
         galtan_marker = galtan_ok ? "" : "*"
 
-        @printf("   %-15s econ=%.2f%s [%.2f-%.2f]  galtan=%.2f%s [%.2f-%.2f]  %s\n",
+        @printf("   %-15s economic=%.2f%s [%.2f-%.2f]  cultural=%.2f%s [%.2f-%.2f]  %s\n",
             anchor.name, mean_econ, econ_marker, anchor.econ[1], anchor.econ[2],
             mean_galtan, galtan_marker, anchor.galtan[1], anchor.galtan[2], status)
     end
@@ -845,9 +845,9 @@ function save_output(output::DataFrame, metadata::Dict, segment_info::Union{Data
         if is_2d
             println(f, "Model: 2D Direct Bipolar")
             println(f, "")
-            println(f, "Bipolar scales (0 = left/GAL, 1 = right/TAN):")
+            println(f, "Bipolar scales (0 = left/cosmopolitan, 1 = right/traditionalist):")
             println(f, "  economic_lr: Economic left-right position (directly estimated)")
-            println(f, "  galtan: GAL-TAN cultural position (directly estimated)")
+            println(f, "  galtan: Cultural cosmopolitan--traditionalist position (directly estimated)")
             # Note: general_lr is computed internally for cross-dimensional anchoring
             # but not reported as output (the two dimension-specific estimates are preferred)
         else
@@ -856,12 +856,12 @@ function save_output(output::DataFrame, metadata::Dict, segment_info::Union{Data
             println(f, "Dimensions (0 = low, 1 = high):")
             println(f, "  pro_market: Pro-market economic position")
             println(f, "  pro_welfare: Pro-welfare state position")
-            println(f, "  cosmopolitan: Cosmopolitan/GAL cultural position")
+            println(f, "  cosmopolitan: Cosmopolitan cultural position")
-            println(f, "  traditional: Traditional/TAN cultural position")
+            println(f, "  traditional: Traditionalist cultural position")
             println(f, "")
-            println(f, "Derived bipolar scales (0 = left/GAL, 1 = right/TAN):")
+            println(f, "Derived bipolar scales (0 = left/cosmopolitan, 1 = right/traditionalist):")
             println(f, "  economic_lr: Economic left-right (derived from pro_market - pro_welfare)")
-            println(f, "  galtan: GAL-TAN (derived from traditional - cosmopolitan)")
+            println(f, "  galtan: Cultural cosmopolitan--traditionalist (derived from traditional - cosmopolitan)")
         end
         println(f, "")
         println(f, "Uncertainty columns:")

src/julia/validate_construct.jl

@@ -324,7 +324,7 @@ function validate_country_patterns(model::DataFrame)
     sort!(country_stats, :n_obs, rev=true)
 
     @printf("%-4s %6s %6s %8s %8s %8s %8s\n",
-        "CC", "Parties", "N", "Econ", "SD", "Galtan", "SD")
+        "CC", "Parties", "N", "Econ", "SD", "Cult", "SD")
     println("-"^60)
 
     for row in eachrow(country_stats[1:min(20, nrow(country_stats)), :])
@@ -349,9 +349,9 @@ function validate_country_patterns(model::DataFrame)
                 push!(issues, "right-skewed economy")
             end
             if row.mean_galtan < 0.35
-                push!(issues, "cultural-skewed")
+                push!(issues, "cosmopolitan-skewed")
             elseif row.mean_galtan > 0.65
-                push!(issues, "TAN-skewed")
+                push!(issues, "traditionalist-skewed")
             end
             println("  $(row.country): $(join(issues, ", "))")
         end

src/julia/validate_convergent.jl

@@ -201,13 +201,13 @@ function validate_economic_lr(model::DataFrame, expert::DataFrame)
 end
 
 function validate_galtan(model::DataFrame, expert::DataFrame)
-    """Validate galtan against CHES galtan and V-Party/GPS libcons"""
+    """Validate cultural cosmopolitan--traditionalist estimates against CHES and V-Party/GPS cultural measures"""
 
     println("\n" * "="^60)
-    println("CONVERGENT VALIDITY: galtan")
+    println("CONVERGENT VALIDITY: cultural cosmopolitan--traditionalist")
     println("="^60)
 
-    # Filter expert data for GAL-TAN dimension
+    # Filter expert data for cultural cosmopolitan--traditionalist dimension
     galtan_vars = filter(v -> occursin("galtan", v) || occursin("libcon", v), unique(expert.var))
     galtan_expert = filter(row -> row.var in galtan_vars, expert)
 
@@ -338,17 +338,17 @@ function validate_discriminant(model::DataFrame, expert::DataFrame)
         @printf("  Model Economic  × Expert Economic:  r = %.3f (convergent, n=%d)\n", r, nrow(merged_ee))
     end
 
-    # 2. Expert economic vs Model galtan (discriminant)
+    # 2. Expert economic vs model cultural dimension (discriminant)
     merged_eg = innerjoin(econ_expert, model_gal, on=[:party, :year])
     if nrow(merged_eg) >= 10
         r = cor(merged_eg.val, merged_eg.galtan)
         push!(results, (model_dim="galtan", expert_dim="economic",
                         r_pearson=r, r_spearman=corspearman(merged_eg.val, merged_eg.galtan),
                         n=nrow(merged_eg), type="discriminant"))
-        @printf("  Model GAL-TAN   × Expert Economic:  r = %.3f (discriminant, n=%d)\n", r, nrow(merged_eg))
+        @printf("  Model Cultural  × Expert Economic:  r = %.3f (discriminant, n=%d)\n", r, nrow(merged_eg))
     end
 
-    # 3. Expert galtan vs Model galtan (convergent - already computed, include for matrix)
+    # 3. Expert cultural vs model cultural dimension (convergent - already computed, include for matrix)
     galtan_vars = filter(v -> occursin("galtan", v) || occursin("libcon", v), unique(expert.var))
     galtan_expert = filter(row -> row.var in galtan_vars, expert)
     merged_gg = innerjoin(galtan_expert, model_gal, on=[:party, :year])
@@ -357,22 +357,22 @@ function validate_discriminant(model::DataFrame, expert::DataFrame)
         push!(results, (model_dim="galtan", expert_dim="galtan",
                         r_pearson=r, r_spearman=corspearman(merged_gg.val, merged_gg.galtan),
                         n=nrow(merged_gg), type="convergent"))
-        @printf("  Model GAL-TAN   × Expert GAL-TAN:   r = %.3f (convergent, n=%d)\n", r, nrow(merged_gg))
+        @printf("  Model Cultural  × Expert Cultural:  r = %.3f (convergent, n=%d)\n", r, nrow(merged_gg))
     end
 
-    # 4. Expert galtan vs Model economic (discriminant)
+    # 4. Expert cultural vs model economic dimension (discriminant)
     merged_ge = innerjoin(galtan_expert, model_econ, on=[:party, :year])
     if nrow(merged_ge) >= 10
         r = cor(merged_ge.val, merged_ge.economic_lr)
         push!(results, (model_dim="economic_lr", expert_dim="galtan",
                         r_pearson=r, r_spearman=corspearman(merged_ge.val, merged_ge.economic_lr),
                         n=nrow(merged_ge), type="discriminant"))
-        @printf("  Model Economic  × Expert GAL-TAN:   r = %.3f (discriminant, n=%d)\n", r, nrow(merged_ge))
+        @printf("  Model Economic  × Expert Cultural:  r = %.3f (discriminant, n=%d)\n", r, nrow(merged_ge))
     end
 
     println()
     println("MTMM Matrix:")
-    println("                    Expert Economic    Expert GAL-TAN")
+    println("                    Expert Economic    Expert Cultural")
     for r in results
         if r.model_dim == "economic_lr" && r.expert_dim == "economic"
             @printf("  Model Economic:      %.3f            ", r.r_pearson)
@@ -385,7 +385,7 @@ function validate_discriminant(model::DataFrame, expert::DataFrame)
     end
     for r in results
         if r.model_dim == "galtan" && r.expert_dim == "economic"
-            @printf("  Model GAL-TAN:       %.3f            ", r.r_pearson)
+            @printf("  Model Cultural:      %.3f            ", r.r_pearson)
         end
     end
     for r in results

src/julia/validate_uncertainty.jl

@@ -87,7 +87,7 @@ function load_chains_selective(run_dir::String, needed_rr::Set{Int}, K::Int)
     # Build the set of column names we need
     needed_cols = Set{String}()
 
-    # theta columns: economic_lr.{rr} and galtan.{rr} for each unique rr
+    # theta columns: economic_lr.{rr} and galtan.{rr} (cultural cosmopolitan--traditionalist) for each unique rr
     for rr in needed_rr
         push!(needed_cols, "economic_lr.$rr")
         push!(needed_cols, "galtan.$rr")
@@ -384,6 +384,7 @@ function summarize_coverage(result::DataFrame, ci_level::Float64)
 
     # Overall by dimension
     dim_names = Dict(1 => "economic_lr", 2 => "galtan")
+    display_dim_names = Dict(1 => "economic left-right", 2 => "cultural cosmopolitan--traditionalist")
 
     summary_rows = []
     for dim in sort(unique(result.dim_idx))
@@ -394,8 +395,8 @@ function summarize_coverage(result::DataFrame, ci_level::Float64)
         ci = wilson_ci(ppc, n)
         dim_name = dim_names[dim]
 
-        println(@sprintf("\n  %-15s: %.1f%% [%.1f%%, %.1f%%]  (%d/%d)",
+        println(@sprintf("\n  %-40s: %.1f%% [%.1f%%, %.1f%%]  (%d/%d)",
-            dim_name, 100*ppc, 100*ci.lower, 100*ci.upper, n_covered, n))
+            display_dim_names[dim], 100*ppc, 100*ci.lower, 100*ci.upper, n_covered, n))
 
         push!(summary_rows, (
             dimension = dim_name,