Sex and Gender Identity: Data Collection and Language Considerations for Human Research Ethics Committees and Researchers
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.753 | 0.725 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".