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wassnameandClaudypoo
576da38830
Per feedback (brief + inline + where seen, not a shadow file): - rm mfv_country_factors_SOURCES.md; MFV per-source citations+transforms now in read_human_mfv() docstring (deep detail stays in each row's commit body). - instruments.py: one-line human_csv source per legacy dataset (mfq2/big5/16pf/humor) above _SPECS -- Atari 2023, OpenPsychometrics IPIP, Schermer 2020. - value_axes.py: add years+DOIs to the custom-axis Sources block (Graham/Haidt 2009, DeYoung 2007, Atari 2023, Martin 2003, Inglehart-Welzel). iw_axes already documents WVS. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
70 lines
3.7 KiB
Python
70 lines
3.7 KiB
Python
"""Named 2-axis "value map" groupings per instrument -- the interpretable alternative to the blind
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ipsative PCA map. Each instrument gets two axes with FOUR named pole directions (like the WVS
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Inglehart-Welzel map), so a point's position reads directly. -- authored by Claude
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An axis is a list of (factor, sign): the axis score of a 0-1 fraction profile is the mean over its
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factors of the endorsement (sign +1) or its complement 1-endorsement (sign -1). So a factor entered
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with -1 is reverse-scored (big5 Stability reverses neuroticism; a contrast axis puts one pole's
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factors at -1). High score = the axis's POSITIVE (second) pole.
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Sources (axis groupings aggregated from these papers):
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- MFT individualizing/binding: Graham, Haidt & Nosek 2009, doi 10.1037/a0015141.
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- MFQ-2 equality/proportionality fairness split: Atari et al. 2023 "Morality beyond the WEIRD".
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- Big Five meta-traits Plasticity/Stability: DeYoung, Quilty & Peterson 2007, doi 10.1037/0022-3514.93.5.880.
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- HSQ 2x2 adaptive/maladaptive x self/other: Martin et al. 2003, doi 10.1016/S0092-6566(02)00534-2.
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- WVS: Inglehart & Welzel 2005 (see tinymfv.iw_axes -- the WVS map builds its own item-level axes;
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this table is for the psychometric instruments).
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The debatable calls (flagged): the SECOND MFT axis is not canonical -- mfq2 uses the documented
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equality(egalitarian) vs proportionality(meritocratic) fairness split; mfv (no equality/proportionality
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factors) uses liberty(autonomy) vs authority(hierarchy). Rename/retune freely; this is just data.
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"""
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from __future__ import annotations
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import numpy as np
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# instrument -> (x_axis, y_axis); each axis = (neg_pole_label, pos_pole_label, [(factor, sign)]).
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VALUE_AXES: dict[str, tuple] = {
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"mfq2": (
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("Individualizing", "Binding",
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[("care", -1), ("equality", -1), ("proportionality", -1),
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("loyalty", 1), ("authority", 1), ("purity", 1)]),
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("Equality", "Proportionality", [("equality", -1), ("proportionality", 1)]),
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),
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"mfv": (
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("Individualizing", "Binding",
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[("care", -1), ("fairness", -1), ("liberty", -1),
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("authority", 1), ("loyalty", 1), ("sanctity", 1)]),
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("Liberty", "Authority", [("liberty", -1), ("authority", 1)]),
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),
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"big5": (
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# DeYoung's meta-traits are unipolar, but name BOTH ends so every axis reads as a contrast
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# (like the others): low Plasticity = reserved/conventional; low Stability = volatile.
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("Reserved", "Exploratory", [("extraversion", 1), ("openness", 1)]),
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("Volatile", "Stable",
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[("agreeableness", 1), ("conscientiousness", 1), ("neuroticism", -1)]),
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),
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"humor_styles": (
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("Maladaptive", "Adaptive",
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[("affiliative", 1), ("selfenhancing", 1), ("aggressive", -1), ("selfdefeating", -1)]),
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("Other-directed", "Self-directed",
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[("selfenhancing", 1), ("selfdefeating", 1), ("affiliative", -1), ("aggressive", -1)]),
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),
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}
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def axis_score(profile_frac: np.ndarray, dims: list[str], axis: list[tuple]) -> float:
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"""Mean signed endorsement over an axis's factors (sign -1 -> 1 - endorsement). Input is a 0-1
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fraction profile in `dims` order."""
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idx = {d: i for i, d in enumerate(dims)}
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vals = [profile_frac[idx[f]] if s > 0 else 1.0 - profile_frac[idx[f]] for f, s in axis]
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return float(np.mean(vals))
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def value_coords(M: np.ndarray, dims: list[str], name: str) -> tuple[np.ndarray, tuple[str, str, str, str]]:
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"""(P[n,2], pole_labels) for instrument `name`: project each 0-1 fraction row of M onto its two
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named value axes. pole_labels = (x_neg, x_pos, y_neg, y_pos)."""
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(xn, xp, xa), (yn, yp, ya) = VALUE_AXES[name]
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P = np.array([[axis_score(row, dims, xa), axis_score(row, dims, ya)] for row in M])
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return P, (xn, xp, yn, yp)
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