Establish P4 WVS map data foundation (GlobalOpinionQA)

Probe + artifact: the WVS subset of Anthropic/llm_global_opinions is 353 questions
over 90 countries (212 questions with >=40 countries), matching tinymfv's MC +
human-anchor shape and dense enough for an Economist-scale map. Documents the
selections parse recipe and the open axis-definition fork (literal IW 10-question
factor model vs shared-question ipsative PCA) before model-run compute.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

scripts/probe_wvs_foundation.py

@@ -0,0 +1,49 @@
+"""P4 data foundation: what the GlobalOpinionQA WVS subset gives us for a WVS/Inglehart-Welzel map.
+
+Anthropic/llm_global_opinions is MC questions with per-country human answer distributions -- almost
+exactly tinymfv's instrument shape (allowed answer tokens + human anchors). This probe quantifies
+the WVS subset's coverage and prints the parse recipe, so we know a country x question matrix is
+dense enough to place models among human societies. No model runs here.
+
+  uv run python scripts/probe_wvs_foundation.py
+"""
+from __future__ import annotations
+
+import ast
+import re
+from collections import Counter
+
+import numpy as np
+from datasets import load_dataset
+
+
+def parse_selections(s: str) -> dict[str, list[float]]:
+    """`selections` ships as a repr of a defaultdict; pull the dict literal out and eval it safely."""
+    return ast.literal_eval(re.search(r"\{.*\}", s, re.S).group(0))
+
+
+def main() -> None:
+    ds = load_dataset("Anthropic/llm_global_opinions", split="train")
+    by_source = Counter(ds["source"])
+    wvs = [r for r in ds if r["source"] == "WVS"]
+    ncountry, allc = [], Counter()
+    for r in wvs:
+        sel = parse_selections(r["selections"])
+        ncountry.append(len(sel))
+        allc.update(sel.keys())
+    print(f"source split: {dict(by_source)}")
+    print(f"WVS questions: {len(wvs)}")
+    print(f"distinct countries: {len(allc)}")
+    print(f"countries/question min/median/max: {min(ncountry)}/{int(np.median(ncountry))}/{max(ncountry)}")
+    print(f"questions with >=40 countries: {sum(n >= 40 for n in ncountry)}")
+    print(f"top countries by coverage: {allc.most_common(12)}")
+    r = wvs[0]
+    sel = parse_selections(r["selections"])
+    c0 = next(iter(sel))
+    print("\nexample question:", repr(r["question"])[:160])
+    print("options:", ast.literal_eval(r["options"]) if isinstance(r["options"], str) else r["options"])
+    print(f"human dist for {c0}:", sel[c0])
+
+
+if __name__ == "__main__":
+    main()