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data provenance: inline at point-of-use, drop standalone SOURCES.md
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>
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wassname
@@ -245,9 +245,14 @@ def _zscore(v: np.ndarray) -> np.ndarray:
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def read_human_mfv() -> tuple[list[str], dict[str, dict[str, float]]]:
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"""(countries, {country: {foundation: mean_1to5}}) from the bundled MFV human norms.
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JimenezLeal2025 (LatAm: Argentina/Colombia/Peru/US) + Yamada2025 (MFV-J: Japan)
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+ Hopp2024 (Dutch: Netherlands) + Marques2020 (Brazil) + Crone2021 (Australia):
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8 countries x 6 foundations (no Social Norms). Provenance: mfv_country_factors_SOURCES.md."""
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8 countries x 6 foundations (no Social Norms). Per-row provenance = the CSV `source`
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column; each tag expands here (full transform detail lives in the row's git commit body):
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JimenezLeal2025_LatAm AR/CO/PE/US Jimenez-Leal+ 2025 Collabra doi 10.1525/collabra.128178 (tables)
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Yamada2025_MFV-J Japan Yamada+ 2026 Jpn J Psych doi 10.4992/jjpsy.97.24228 (table)
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Hopp2024_DutchMFV Netherlands Hopp+ 2024 JDM 19:e10 doi 10.1017/jdm.2024.5 (Table 1; care=mean(phys,emo))
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Marques2020_..._affinecal Brazil Marques+ 2020 JDM journal.sjdm.org/19/190809a; Fig-3 digitized + affine bias-cal
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Crone2021_AusUndergrad Australia Crone,Rhee,Laham 2021 Behav Res Methods doi 10.3758/s13428-020-01489-y;
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raw 90-item dat_rep.sav (OSF cmwpv, 756 undergrads), NOT the GA-abbrev subset"""
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path = T.maps.DATA / "human" / "mfv_country_factors.csv"
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by_country: dict[str, dict[str, float]] = {}
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with open(path, newline="") as fh:
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@@ -1,95 +0,0 @@
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# Sources & provenance: `mfv_country_factors.csv`
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Audit trail for the human country rows in the MFV value map. One entry per `source`
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tag in the CSV. Each entry records: full citation, URL/DOI, exactly which table/figure
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the numbers came from, and any data transformation applied (so a reviewer can reproduce
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the row from the primary source).
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Author of these notes: Claude (pair-programming with wassname), not wassname.
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## Instrument & how the map uses these numbers
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All rows are the Clifford et al. (2015) Moral Foundations Vignettes (MFV): 2nd-person
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"You see ..." vignettes rated for moral wrongness on a 1-5 scale, coded to six
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foundations (care, fairness, liberty, authority, loyalty/ingroup, sanctity/purity).
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The value map (`scripts/plot_steer_showcase.py::read_human_mfv`) reads **only the `mean`
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column**, then **z-scores each country across its six foundations** (ipsative). Because
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z-scoring is affine-invariant, any per-country scale/offset (translation bias, digitizing
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bias, differing scale anchors) cancels: the map shows *relative* foundation emphasis, not
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a calibrated cross-country ranking. Absolute means/SDs are still stored honestly for any
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non-map reuse.
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Caveat carried by two of the source papers (Jimenez-Leal, Hopp): the MFV shows
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measurement non-invariance / differential item functioning across countries, so raw
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between-country mean comparisons are a rough reference, not a validated ranking.
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## Sources
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### `JimenezLeal2025_LatAm` -- Argentina, Colombia, Peru, US
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- Jimenez Leal, W., Carmona, G., Murray, S., & Amaya, S. (2025). Validation of the Moral
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Foundation Vignettes in Latin America. *Collabra: Psychology*, 11(1), 128178.
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- DOI: https://doi.org/10.1525/collabra.128178 (open access, CC BY)
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- Numbers: per-country foundation means/SD/N from the paper's descriptive tables
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(N = 1,650 across 3 Latin-American countries via polling agency, plus a US comparison).
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- Transformation: none (means used as tabulated on the native 1-5 scale).
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### `Yamada2025_MFV-J` -- Japan
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- Yamada, J., Nakawake, Y., & Suyama, M. (2026). Developing a Japanese version of the
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Moral Foundations Vignettes (MFV-J). *The Japanese Journal of Psychology*.
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(Tag says 2025 = preprint/advance-pub year; journal assigns 2026.)
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- DOI: https://doi.org/10.4992/jjpsy.97.24228 (advance publication PDF, open access)
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- Numbers: MFV-J foundation means/SD, N = 564, from the paper's descriptive table.
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- Transformation: none.
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### `Hopp2024_DutchMFV` -- Netherlands
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- Hopp, F. R., Jargow, B., Kouwen, E., & Bakker, B. N. (2024). The Dutch moral foundations
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stimulus database. *Judgment and Decision Making*, 19, e10.
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- DOI: https://doi.org/10.1017/jdm.2024.5 (open access, CC BY). OSF: https://osf.io/9gnza/
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- Numbers: foundation means + 95% CIs from the paper's Table 1 (N = 586 Dutch crowdworkers,
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120 translated MFVs). Per-foundation N varies by item allocation (that's why the CSV N
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differs per row).
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- Transformation: **Care collapsed** from the paper's split physical-care (4.09) +
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emotional-care (3.53) into a single care = 3.81 (mean of the two). Other foundations
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taken as tabulated. SD/SE/CI from the paper's reported CIs.
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### `Marques2020_BrazilMFV_fig3digitized_affinecal` -- Brazil
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- Marques, L. M., et al. (2020). Translation and validation of the Moral Foundations
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Vignettes for the Portuguese language in a Brazilian sample. *Judgment and Decision
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Making*.
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- URL: http://journal.sjdm.org/19/190809a/jdm190809a.html
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PDF: http://journal.sjdm.org/19/190809a/jdm190809a.pdf
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- Numbers: the paper tabulates no per-foundation means, so means were **digitized from
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Figure 3** (Brazil series) with WebPlotDigitizer by wassname. N = 494 (paper).
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- Transformations (two, both by Claude):
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1. **Care collapsed** from digitized Care-E + Care-P into one care value.
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2. **Affine bias-correction** of all digitized means to two paper-stated Purity anchors
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(Brazil purity 3.45, Clifford US purity 3.85; paper text): `true = 0.9155*digitized
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+ 0.228`. Reproduces both anchors; Brazil purity lands exactly 3.45. SDs recovered
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from the digitized 95% CI whiskers (Fig 3 caption): `sd = halfwidth/1.96 * sqrt(494)`;
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each whisker pair's midpoint matches its mean to <=0.005 (validated). SDs scaled by
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the affine slope. Provably map-neutral (max |dz| = 0.017, rounding only).
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- Digitized source figure staged at `docs/digitize/brazil_fig3_page-08.png`.
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### `Crone2021_AusUndergrad_MFV90raw` -- Australia
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- Crone, D. L., Rhee, J. J., & Laham, S. M. (2021). Developing brief versions of the Moral
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Foundations Vignettes using a genetic algorithm-based approach. *Behavior Research
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Methods*, 53(3), 1179-1187.
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- DOI: https://doi.org/10.3758/s13428-020-01489-y . OSF: https://osf.io/cmwpv/
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(component "Data" = https://osf.io/nv4ty/ , file `dat_rep.sav`).
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- Sample: 756 Australian undergraduates (complete cases). The paper's other sample is
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580 US MTurk workers (`dat_amt`), NOT used here -- that would duplicate the US row.
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- Numbers: computed by Claude from the **raw participant ratings** in `dat_rep.sav`, the
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full 90-item Clifford MFV (1-5 wrongness), using the author's own item->foundation map
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from `mfv_abbreviation.Rmd` (Care = MFV 1-27 [physical+emotional+other], Fairness 28-39,
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Liberty 40-50, Authority 51-64, Loyalty/Ingroup 65-80, Sanctity/Purity 81-90).
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Per foundation: mean over participants of each participant's item-mean; SD = between-
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participant SD (ddof=1); SE = SD/sqrt(N); CI = mean +/- 1.96*SE. Complete-case exclusion
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(all 90 items present) reproduces the paper's N = 756 exactly.
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- Transformation: **NOT the genetic-algorithm-abbreviated subset.** The paper's headline
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contribution is a brief MFV chosen by GAabbreviate; we deliberately used the *full*
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90-item ratings so Australia is comparable to the other (full-instrument) country rows.
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Care spans all 27 care items (no collapse needed; it's the natural mean).
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- Reproduce: OSF files fetched via `https://osf.io/download/4psfc/` (dat_rep.sav);
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read with `pyreadstat.read_sav`. (The OSF "Download as zip" gave an empty archive --
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fetch files individually.)
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@@ -32,6 +32,12 @@ DIGITS_1_5 = ["1", "2", "3", "4", "5"]
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PREFILL = "("
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# instrument name -> (survey subdir, {frame: filename stem}, human_csv, display, human_scale_max)
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# human_csv provenance (per-country reference means; ported from the mft_honesty experiment):
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# mfq2 : Atari+ 2023 "Morality beyond the WEIRD", OSF osf.io/srtxn/ Study 2 (19 countries)
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# big5 : OpenPsychometrics IPIP-FFM raw (openpsychometrics.org/_rawdata, BIG5), aggregated (24 countries)
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# 16pf : OpenPsychometrics 16PF raw (openpsychometrics.org/tests/16PF), aggregated (34 countries)
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# humor_styles : Schermer+ 2020 "Humor styles across 28 countries", doi 10.1007/s12144-019-00552-y (Tables 1-2)
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# (MFV is not here -- it's read in scripts/plot_steer_showcase.py::read_human_mfv, provenance in that docstring.)
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_F3 = {"forward": "questionnaire", "inverted": "questionnaire_inverted", "negated": "questionnaire_negated"}
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_SPECS = {
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"mfq2": ("mfq2", {"forward": "forward", "inverted": "inverted", "negated": "negated"},
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@@ -7,10 +7,13 @@ factors of the endorsement (sign +1) or its complement 1-endorsement (sign -1).
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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: MFT individualizing/binding -- Graham & Haidt; MFQ-2 equality/proportionality fairness split
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-- Atari et al. 2023. Big Five meta-traits Plasticity/Stability -- DeYoung 2007. HSQ 2x2
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adaptive/maladaptive x self/other -- Martin et al. 2003. WVS -- Inglehart-Welzel (see tinymfv.iw_axes;
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the WVS map builds its own item-level axes, this table is for the psychometric instruments).
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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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