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Establish P4 WVS map data foundation (GlobalOpinionQA)

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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>
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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).