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wassnameandClaudypoo
469788c418
Import name tinymfv -> moralmaps, pip name tiny-mfv -> moral-maps, GitHub URLs wassname/tinymfv -> wassname/moral-maps. HuggingFace dataset id wassname/tiny-mfv left as-is (separate namespace, published data artifact). Historical docs/spec/* and RESEARCH_JOURNAL keep their dated paths. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
91 lines
4.5 KiB
Python
91 lines
4.5 KiB
Python
"""Instrument registry: build `Instrument` objects (instrument.py schema) from the bundled survey
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data. MFQ-2 plus three reused questionnaires (Big-Five, 16PF, Humor Styles), all ordinal 1-5.
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Ported from the weight_steer_honesty experiment (`mft_honesty.instruments`). Each instrument is
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three framing files (forward / inverted / negated) of the SAME items reworded; we load all three
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into InstrItems tagged with `frame`, so the reader scores every (item, frame) and
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`per_item_categorical` canonicalizes them to one forward distribution per item.
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Per-instrument variation:
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- data dir + framing filenames (MFQ-2: forward/inverted/negated.json; the others:
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questionnaire{,_inverted,_negated}.json).
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- dimensions: derived from the forward file in first-appearance order.
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- keying: MFQ-2 items are all +1; IPIP/HSQ are mixed-key within a factor, sign read from
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keying.json (written by the experiment's human-IPIP pipeline). Reverse items are reflected at
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reduce_ordinal time, not here.
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- human reference scale: all administered at 1-5; the HSQ human reference is on 1-7
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(`human_scale_max`), reconciled to a 0-1 fraction at MAP time, not here. Per-item human_label is
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left None (the maps use the per-country CSVs in `data/human/`, not per-item histograms).
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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from .instrument import Instrument, InstrItem
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DATA = Path(__file__).resolve().parent / "data"
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SURVEYS = DATA / "surveys"
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HUMAN = DATA / "human"
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DIGITS_1_5 = ["1", "2", "3", "4", "5"]
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# Same response-format prefill the experiment used: the model answers "(N)", we prefill "(".
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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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"mfq2_country_foundations.csv", "Moral Foundations", 5),
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"big5": ("big5", _F3, "big5_country_factors.csv", "Big Five", 5),
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"16pf": ("16pf", _F3, "16pf_country_factors.csv", "16PF", 5),
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"humor_styles": ("humor_styles", _F3, "humor_styles_country_factors.csv", "Humor Styles", 7),
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}
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def _load_keying(survey_dir: Path, default_forward_ids: list[str]) -> dict[str, int]:
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p = survey_dir / "keying.json"
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if not p.exists():
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return {i: 1 for i in default_forward_ids} # MFQ-2: every item is +1
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return {str(k): int(v) for k, v in json.loads(p.read_text()).items()}
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def build_instrument(name: str) -> Instrument:
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sub, frame_files, human_csv, display, hmax = _SPECS[name]
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sdir = SURVEYS / sub
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frames = {fr: json.loads((sdir / f"{stem}.json").read_text())[0] for fr, stem in frame_files.items()}
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fwd = frames["forward"]
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dims: list[str] = [] # subscales in first-appearance order (forward file)
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for q in fwd["questions"]:
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if q["dimension"] not in dims:
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dims.append(q["dimension"])
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keying = _load_keying(sdir, [str(q["id"]) for q in fwd["questions"]])
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items: list[InstrItem] = []
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for fr, doc in frames.items():
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for q in doc["questions"]:
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items.append(InstrItem(
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id=str(q["id"]), prompt=q["content"], dimension=q["dimension"],
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sign=keying[str(q["id"])], frame=fr, human_label=None,
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meta={"task": doc["task"]})) # frame-specific response-scale legend
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return Instrument(
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name=name, construct="endorsement", kind="ordinal",
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answer_space=DIGITS_1_5, dimensions=dims, items=items, prefill=PREFILL,
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scale_max=5, human_scale_max=hmax,
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display=display, human_csv=str(HUMAN / human_csv))
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INSTRUMENTS: dict[str, Instrument] = {}
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def get(name: str) -> Instrument:
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if name not in INSTRUMENTS:
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INSTRUMENTS[name] = build_instrument(name)
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return INSTRUMENTS[name]
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