Sex and gender terminology: a glossary for gender-inclusive epidemiology
Bibliographic record
Abstract
There is increased interest in inclusion, diversity and representativeness in epidemiological and community health research. Despite this progress, misunderstanding and conflation of sex and gender have precluded both the accurate description of sex and gender as sample demographics and their inclusion in scientific enquiry aiming to distinguish health disparities due to biological systems, gendered experiences or their social and environmental interactions. The present glossary aims to define and improve understanding of current sex-related and gender-related terminology as an important step to gender-inclusive epidemiological research. Effectively, a proper understanding of sex, gender and their subtleties as well as acknowledgement and inclusion of diverse gender identities and modalities can make epidemiology not only more equitable, but also more scientifically accurate and representative. In turn, this can improve public health efforts aimed at promoting the well-being of all communities and reducing health inequities.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".