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MFV human map: add Netherlands (Hopp et al. 2024 Dutch MFV, N=586)

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Clifford MFV wrongness vignettes, 1-5 scale (same as existing LatAm/Japan
rows). Care collapsed from their split physical(4.09)+emotional(3.53) to a
single 3.81; other foundations from their Table 1 with paper 95% CIs.
Grows the human country cloud 5 -> 6.

Note: the Dutch and LatAm authors both flag MFV measurement non-invariance
(DIF) across countries, so between-country means are a rough reference, not
a calibrated ranking. Map is ipsative (within-country z), which is affine-
invariant to the scale, so it reads relative foundation emphasis.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-07-05 10:01:52 +08:00
co-authored by Claudypoo
parent fe6815a9c7
commit f7bbdc381f
2 changed files with 16 additions and 6 deletions
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scripts/plot_steer_showcase.py
+10 -6
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@@ -210,15 +210,18 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float,
plt.close(figm)
# Alternative NAMED-AXIS value map (interpretable poles, no compass/minimap): project the
# societies + the AI base/steered points onto the instrument's two named value axes.
# societies + the AI base/steered points onto the instrument's two named value axes, and draw the
# steer as a CONNECTED base->+c/-c path (same visual language as the ipsative map's trajectory).
from tinymfv.value_axes import VALUE_AXES, value_coords, axis_score
if name in VALUE_AXES:
Pval, poles = value_coords(Mfrac, dims, name)
(_, _, xa), (_, _, ya) = VALUE_AXES[name]
ai = {lab: (axis_score(_frac(v, instr.scale_max), dims, xa),
axis_score(_frac(v, instr.scale_max), dims, ya))
for lab, v in zip(labels, (base, pos, neg))}
figv = T.maps.plot_value_map(instr.display, countries, Pval, poles, models=ai, emphasize=emph,
def _vscore(v):
fv = _frac(v, instr.scale_max)
return axis_score(fv, dims, xa), axis_score(fv, dims, ya)
steer = {k: (*_vscore(v), lab) for k, v, lab in
[("base", base, labels[0]), ("pos", pos, labels[1]), ("neg", neg, labels[2])]}
figv = T.maps.plot_value_map(instr.display, countries, Pval, poles, steer=steer, emphasize=emph,
title=f"{instr.display}: value map, LLM steered for {vec_label}")
paths.append(T.maps.save_both(figv, out / name, "map_value"))
plt.close(figv)
@@ -242,7 +245,8 @@ def _zscore(v: np.ndarray) -> np.ndarray:
def read_human_mfv() -> tuple[list[str], dict[str, dict[str, float]]]:
"""(countries, {country: {foundation: mean_1to5}}) from the bundled MFV human norms.
JimenezLeal2025 (LatAm) + Yamada2025 (MFV-J): 5 countries x 6 foundations (no Social Norms)."""
JimenezLeal2025 (LatAm) + Yamada2025 (MFV-J) + Hopp2024 (Dutch): 6 countries x 6 foundations
(no Social Norms)."""
path = T.maps.DATA / "human" / "mfv_country_factors.csv"
by_country: dict[str, dict[str, float]] = {}
with open(path, newline="") as fh: