The Language of Endometriosis Prevalence: How Can Gender Inclusivity and Accuracy Coexist?
Bibliographic record
Abstract
This paper invites and presents critical discussions relevant to how accuracy and gender inclusivity can be fostered when reporting and discussing endometriosis prevalence. While there has been increased awareness of the importance of inclusive language within the endometriosis community, certain language can also communicate inaccuracies and have other unintended consequences that can undermine the health of transgender, gender-diverse, intersex, and cisgender people. Using a cooperative inquiry methodology, this topic is explored through canvasing endometriosis definitions found in academic literature, social and digital media, and digital health care platforms. Through this research, we explore the challenges associated with defining “whom” endometriosis affects, describe the current barriers to use of accurate language, and provide some possible solutions to inaccuracies.
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 | 0.143 | 0.325 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".