Sex and Gender Identity: Data Collection and Language Considerations for Human Research Ethics Committees and Researchers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Including women in research and collecting and disaggregating data on sex is an ethical imperative. However, increasingly gender identity is being prioritised over sex in data collection and language which has ethical implications. In this paper, the authors share their experiences as study participants; a health consumer advocate, patient research advisor, and lay researcher; and academic researchers of engaging with researchers, Human Research Ethics Committees (HRECs), university ethics offices, and editors and reviewers of journals regarding data collection and communication on sex and gender identity. We argue that HRECs, researchers, and publishers must carefully consider the implications of omitting data collection on sex, mandatory and universalising gender identity questions and use of desexed language. We also propose that reduced data collection and disaggregation by sex, universal imposition of gender identity, and use of desexed language in research is decreasing data quality, reducing the willingness of some to participate in research and is culturally imperialistic. Recommendations for HRECs are made and research needs in relation to sex and gender identity are outlined. Respect for women in the conduct of research requires their sex-related experiences and needs are considered and therefore that data on sex is appropriately collected and reported upon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it