DATASHEET.md

MWI — Muslim Women's Interviews Dataset

A machine-readable corpus of 25 de-identified qualitative interviews with Muslim women in the US, Canada and France, structured for use in LLM evaluation.

Version 1.0 · Built 2026-08-27 · Derived from Cleaned, Redacted Transcripts/

---

Files

File	Count	What it is
interviews/MWI-###.md	25	One Markdown file per interview. The readable view.
interviews.jsonl	585	The same content as structured rows: segment_id, participant_id, country, question_id, question, answer. Generated from the Markdown; every answer matches verbatim.
README.md	—	What it is, how to parse it, how to cite it.
DATASHEET.md	—	This file: provenance, limitations, ethics.
LICENSE.md / CITATION.cff	—	CC BY 4.0 terms and citation metadata.

585 question/answer exchanges, ~144,000 words. Each answer carries a segment_id (unique corpus-wide) and a question_id (shared across interviews for the 19 recurring protocol questions) in an HTML comment, so the files parse without guesswork — see the README for a parser.

Composition

• 585 segments, 143,074 words, 25 participants
• 19 core questions answered by 15–25 participants each (437 segments)
• 89 follow-up questions, mostly participant-specific (148 segments)
• Countries: US 14 · Canada 8 · France 3; 6 Canadian participants are in Quebec

Segment schema

{
  "segment_id": "MWI-024_s07",
  "participant_id": "MWI-024",
  "segment_index": 7,
  "question_id": "misconceptions",
  "question_text": "WHAT DO PEOPLE GET WRONG ABOUT MUSLIM WOMEN OR HIJAB?",
  "is_core_question": true,
  "answer_text": "…",
  "n_words": 191,
  "n_paragraphs": 2,
  "redactions": [{"token": "[CITY 1]", "category": "CITY", "index": 1, "compound": false}],
  "n_redactions": 1,
  "redaction_categories": ["CITY"],
  "question_redactions": []
}

Note: a question_id can repeat within a participant — interviewers returned to some topics. Use segment_id as the key, not (participant_id, question_id).

Redaction tokens

Identifying details are replaced by typed tokens such as [CITY 1], [UNIVERSITY 2], [MASJID 1], [INTERVIEWEE]. Numbering restarts per participant — [CITY 1] in MWI-004 and in MWI-021 are different cities. Tokens are not a cross-participant key.

Compound tokens ([CITY 1, STATE 1]) are decomposed into components in redactions while token preserves the original span.

Retained by design: country names, Quebec, France, the US, Quebec legal and identity vocabulary (Bill 21/94/9, CAQ, CEGEP, laïcité, Québécois), ethnicity and nationality descriptors, and public figures cited as commentary. Full policy and the complete change log are in ../Cleaned, Redacted Transcripts/REDACTION-NOTES.md.

Intended uses

This is a corpus release, not a benchmark. Three evaluation designs it supports well:

1. Flattening / heterogeneity — does a model reproduce the range of views 25 real women hold, or collapse them into one voice? The corpus's internal disagreement is the ground truth. Best-supported design.
2. Misconception rejection — grounded in the 22 misconceptions and 15 responding_to_portrayals segments, where participants state directly what is false about them. Ground truth is participant-authored, not researcher-assumed.
3. Perspective faithfulness — compare model answers against the thematic distribution of real answers to the same question.

Of direct relevance: the follow-up question "WHO GETS TO REPRESENT MUSLIM WOMEN IN RESEARCH, MEDIA, OR AI?" (followup__who_gets_to_represent_muslim_women_in_research) was asked of 4 participants — their own views on this dataset's use case.

Uses to avoid

• Treating any participant as representative. n=25, purposively sampled, three countries. The corpus documents variation; it does not estimate population parameters.
• Reading tokens as linkable IDs across participants (see above).

Limitations and biases

• Not a probability sample. Recruitment ran partly through Muslim women's organisations, which over-represents community-involved and civically active women. Two participants hold public community roles.
• Highly educated skew — several physicians, PhD candidates, teachers and graduate students; few low-income or non-English-speaking participants.
• Interviewer effects — follow-up questions differ by participant, so absence of a theme in a transcript does not mean the participant lacked a view on it.
• Prose is edited. Source transcripts were cleaned into readable narrative before redaction; these are not verbatim disfluent speech and should not be used for conversational-realism or ASR work.
• English throughout, including for the French participants; some phrasing is translated.

Ethics and consent

Participants consented to public release for the purpose of shaping LLM training, outputs and analysis. De-identification was performed and independently audited before this dataset was built; the audit and every content-level edit are logged in REDACTION-NOTES.md.

Content. Individual interviews touch on bereavement, pregnancy loss, mental-health disclosure, workplace discrimination, street harassment, and war and political violence.

Contamination caveat

Public release means this text will enter future training corpora. Any benchmark built directly on these verbatim segments will decay as models memorise it. If you need a durable benchmark, derive paraphrased probes and hold the verbatim set back as a contamination check, or version and date each release.

Provenance

Raw interviews → cleaned into readable narrative → redaction and independent audit → these files. Segmentation into question/answer units was verified lossless: every word of the redacted prose appears in exactly one answer.

A fuller redaction log — the policy applied, every content-level edit, and the re-identification audit — is held by the research team and is available on request.