Gender-inclusive writing for epidemiological research on pregnancy
Bibliographic record
Abstract
People who have a uterus but are not cisgender women may carry pregnancies. Unfortunately, to date, academic language surrounding pregnancy remains largely (cis) woman-centric. The exclusion of gender-diverse people in the language of pregnancy research in English is pervasive. In reviewing a random sample of 500 recent articles on pregnancy or pregnant populations across health research fields, we found that only 1.2% of articles used gender-inclusive language (none of them in epidemiology), while the remaining 98.8% used (cis) woman-centric language. First and foremost, recent recommendations highlight the need to include trans, non-binary and gender-diverse people in study design. Meanwhile, there remains a lack of awareness that all research on pregnancy can contribute to inclusiveness, including in dissemination and retroactive description. We explain how the ubiquitous use of (cis) woman-centric language in pregnancy-related research contributes to (1) the erasure of gender diversity; (2) inaccurate scientific communication and (3) negative societal impacts, such as perpetuating the use of exclusionary language by students, practitioners, clinicians, policy-makers and the media. We follow with recommendations for gender-inclusive language in every section (ie, introductions, methods, results, discussions) of epidemiological articles on pregnant populations. The erasure of gender-diverse people in the rhetoric of research about pregnant people can be addressed immediately, including in the dissemination of results from ongoing studies that did not take gender diversity into consideration. This makes gender-inclusive language a crucial first step towards the inclusion of gender-diverse people in epidemiological research on pregnant people and other health research more globally.
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.
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 | 0.184 | 0.597 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.034 | 0.017 |
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".