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wassnameandClaudypoo
40d08f8d7a
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>
50 lines
1.9 KiB
Python
50 lines
1.9 KiB
Python
"""P4 data foundation: what the GlobalOpinionQA WVS subset gives us for a WVS/Inglehart-Welzel map.
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Anthropic/llm_global_opinions is MC questions with per-country human answer distributions -- almost
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exactly tinymfv's instrument shape (allowed answer tokens + human anchors). This probe quantifies
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the WVS subset's coverage and prints the parse recipe, so we know a country x question matrix is
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dense enough to place models among human societies. No model runs here.
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uv run python scripts/probe_wvs_foundation.py
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"""
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from __future__ import annotations
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import ast
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import re
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from collections import Counter
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import numpy as np
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from datasets import load_dataset
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def parse_selections(s: str) -> dict[str, list[float]]:
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"""`selections` ships as a repr of a defaultdict; pull the dict literal out and eval it safely."""
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return ast.literal_eval(re.search(r"\{.*\}", s, re.S).group(0))
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def main() -> None:
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ds = load_dataset("Anthropic/llm_global_opinions", split="train")
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by_source = Counter(ds["source"])
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wvs = [r for r in ds if r["source"] == "WVS"]
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ncountry, allc = [], Counter()
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for r in wvs:
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sel = parse_selections(r["selections"])
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ncountry.append(len(sel))
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allc.update(sel.keys())
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print(f"source split: {dict(by_source)}")
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print(f"WVS questions: {len(wvs)}")
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print(f"distinct countries: {len(allc)}")
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print(f"countries/question min/median/max: {min(ncountry)}/{int(np.median(ncountry))}/{max(ncountry)}")
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print(f"questions with >=40 countries: {sum(n >= 40 for n in ncountry)}")
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print(f"top countries by coverage: {allc.most_common(12)}")
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r = wvs[0]
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sel = parse_selections(r["selections"])
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c0 = next(iter(sel))
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print("\nexample question:", repr(r["question"])[:160])
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print("options:", ast.literal_eval(r["options"]) if isinstance(r["options"], str) else r["options"])
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print(f"human dist for {c0}:", sel[c0])
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if __name__ == "__main__":
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main()
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