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Add MFQ-2 per-zone p90 respondent ellipses to ipsative map

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respondent_profiles now returns countries alongside profiles; plot_ipsative_pca
gains respondent_zones -> a p90 Gaussian ellipse per IW zone of the projected
Atari respondent cloud (edge-only, no scipy). mfq2 uses these real-respondent
ellipses; the other instruments keep country-mean hulls. Journal notes the
finding: individual profiles overlap across cultures (within >> between variance),
so only the country-mean hull reproduces the Economist's clean zone blobs.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-07-04 19:25:52 +08:00
co-authored by Claudypoo
parent 0e7178dfbd
commit 2c2dd9b9bf
3 changed files with 78 additions and 14 deletions
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scripts/plot_steer_showcase.py
+21 -7
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@@ -104,6 +104,13 @@ ECONOMIST_OUTLIERS = {"China", "South Korea", "United States", "Great Britain",
"Nigeria", "Pakistan", "Sweden"}
def _zone_of(country: str) -> str | None:
"""IW zone of a verbatim country string, or None for a known-corrupt row. KeyErrors (fail loud)
on an unrecognised country so a normalization bug can't silently drop it."""
canon = _COUNTRY_CANON.get(country, country)
return None if canon is None else IW_ZONE[canon]
def zones_for(countries: list[str]) -> tuple[dict[str, list[str]], set[str]]:
"""Group verbatim country strings by IW zone + the subset to emphasize. Fails loud (KeyError)
on a country absent from the taxonomy so a name-normalization bug can't silently drop a dot from
@@ -112,12 +119,12 @@ def zones_for(countries: list[str]) -> tuple[dict[str, list[str]], set[str]]:
dropped: list[str] = []
emph: set[str] = set()
for c in countries:
canon = _COUNTRY_CANON.get(c, c)
if canon is None:
z = _zone_of(c)
if z is None:
dropped.append(c)
continue
groups.setdefault(IW_ZONE[canon], []).append(c)
if canon in ECONOMIST_OUTLIERS:
groups.setdefault(z, []).append(c)
if _COUNTRY_CANON.get(c, c) in ECONOMIST_OUTLIERS:
emph.add(c)
if dropped:
logger.warning(f"excluded known-unmapped countries from zone hulls: {dropped}")
@@ -269,16 +276,23 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float,
# fit the ipsative PCA on it (better-conditioned, the true envelope). Other instruments have no raw
# per-person data, so scatter a marginal resample from each country's published mean+sd as the haze
# while keeping the PCA basis on the society means M.
# mfq2 has real per-respondent data -> draw a p90 respondent ELLIPSE per zone (grounded in
# people) and drop the country-mean hull. Other instruments have only society means -> the
# country-mean convex HULL is the best-available zone blob.
_, emph = zones_for(countries)
if name == "mfq2":
respondents, haze = T.maps.respondent_profiles(dims, instr.scale_max), None
resp_countries, respondents = T.maps.respondent_profiles(dims, instr.scale_max)
haze, zones = None, None
respondent_zones = [_zone_of(c) for c in resp_countries]
else:
respondents, haze = None, human_haze(instr)
zones, respondent_zones = zones_for(countries)[0], None
traj = {c: _frac(prof_c[c], instr.scale_max) for c in coh_cs}
zones, emph = zones_for(countries)
figm = T.maps.plot_ipsative_pca(instr, dims, countries, Mfrac,
_frac(base, instr.scale_max), _frac(pos, instr.scale_max),
_frac(neg, instr.scale_max), respondents=respondents, haze=haze,
traj=traj, zones=zones, emphasize=emph, labels=labels)
traj=traj, zones=zones, emphasize=emph,
respondent_zones=respondent_zones, labels=labels)
figm.axes[0].set_title(f"{instr.display}: humans vs LLMs steered for {vec_label}", fontsize=10)
paths = [T.maps.save_both(figm, out / name, "map_pca_ipsative")]
plt.close(figm)