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data provenance: inline at point-of-use, drop standalone SOURCES.md

Browse Source 
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>
This commit is contained in:
wassname
2026-07-05 12:45:14 +08:00
co-authored by Claudypoo
parent bdda475c8c
commit 576da38830
4 changed files with 21 additions and 102 deletions
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scripts/plot_steer_showcase.py
+8 -3
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@@ -245,9 +245,14 @@ 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: Argentina/Colombia/Peru/US) + Yamada2025 (MFV-J: Japan)
+ Hopp2024 (Dutch: Netherlands) + Marques2020 (Brazil) + Crone2021 (Australia):
8 countries x 6 foundations (no Social Norms). Provenance: mfv_country_factors_SOURCES.md."""
8 countries x 6 foundations (no Social Norms). Per-row provenance = the CSV `source`
column; each tag expands here (full transform detail lives in the row's git commit body):
JimenezLeal2025_LatAm AR/CO/PE/US Jimenez-Leal+ 2025 Collabra doi 10.1525/collabra.128178 (tables)
Yamada2025_MFV-J Japan Yamada+ 2026 Jpn J Psych doi 10.4992/jjpsy.97.24228 (table)
Hopp2024_DutchMFV Netherlands Hopp+ 2024 JDM 19:e10 doi 10.1017/jdm.2024.5 (Table 1; care=mean(phys,emo))
Marques2020_..._affinecal Brazil Marques+ 2020 JDM journal.sjdm.org/19/190809a; Fig-3 digitized + affine bias-cal
Crone2021_AusUndergrad Australia Crone,Rhee,Laham 2021 Behav Res Methods doi 10.3758/s13428-020-01489-y;
raw 90-item dat_rep.sav (OSF cmwpv, 756 undergrads), NOT the GA-abbrev subset"""
path = T.maps.DATA / "human" / "mfv_country_factors.csv"
by_country: dict[str, dict[str, float]] = {}
with open(path, newline="") as fh: