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>

docs/reviews/p4_wvs_data_foundation.md

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+# P4 data foundation: GlobalOpinionQA WVS subset for a WVS/Inglehart-Welzel map
+
+Command: `uv run python scripts/probe_wvs_foundation.py`. Date: 2026-07-04.
+
+`Anthropic/llm_global_opinions` is MC questions with per-country human answer distributions -- the
+same shape tinymfv already reads (allowed answer tokens + human anchors), so it drops into our
+reader + ipsative-map machinery.
+
+Coverage of the WVS subset:
+- source split: GAS (Pew) 2203, WVS 353.
+- WVS questions: 353. Distinct countries: 90 (the Economist chart uses 88).
+- countries/question: min 1, median 63, max 90. 212 questions have >=40 countries.
+- top-coverage countries: Kenya, Zimbabwe, Ethiopia, Lebanon, Jordan, Iraq, Nigeria, South Korea,
+  Puerto Rico, Mexico, Malaysia, Colombia (good IW-zone spread).
+- `selections` ships as a `defaultdict(...)` repr string; parse recipe is
+  `ast.literal_eval(re.search(r"\{.*\}", s, re.S).group(0))` (see probe_wvs_foundation.py).
+- `options` includes non-substantive tails ("Don't know", "No answer") that must be dropped or held
+  out of the answer space before renormalizing.
+
+Verdict: dense enough (90 countries x 353 questions, 212 questions with >=40 countries) to place
+models among human societies at Economist scale. This is the human anchor; the model side reuses the
+logprob reader (open models) or read_api sampling reader (frontier models).
+
+## Open research fork (needs a human steer before spending model-run compute)
+
+How to define the two map axes -- this is a research-validity choice, not a mechanical one, so I am
+not guessing it:
+
+- (4a) Literal Inglehart-Welzel: use the 10 documented IW questions (Traditional: Q164, Q7-17, Q184,
+  Q254, Q4; Survival: Q154-155, Q46, Q182, Q209, Q57), factor-analyze to the two published axes,
+  place countries and models on them. Most faithful to the Economist, but fragile: the 10 exact
+  questions may not all be present in GlobalOpinionQA with consistent country coverage, and it
+  hard-codes Inglehart-Welzel's factor structure.
+- (4b) Shared-question ipsative PCA (our existing map method): pick the WVS questions with dense
+  country coverage, build a country x question matrix, ipsative-PCA to 2 PCs (reusing
+  maps.ipsative_pca), and project models administered the same questions. Drops straight into the
+  P1 hull code. Axes are data-derived (not the literal IW Traditional/Survival), so the chart is
+  "WVS-values map" not a verbatim Economist reproduction.
+
+Also to confirm before spending: which model set goes on the map (open-weight via the logprob reader
+vs frontier via read_api sampling), and the country subset (all 90 vs the >=40-coverage 212-question
+core).

scripts/probe_wvs_foundation.py

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+"""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()