First steps for integrating sex and gender considerations into basic experimental biomedical research
Bibliographic record
Abstract
In recent decades there has been an increasing recognition of the need to account for sex and gender in biology and medicine, in order to develop a more comprehensive understanding of biological phenomena and to address gaps in medical knowledge that have arisen due to a generally masculine bias in research. We have noted that as basic experimental biomedical researchers, we face unique challenges to the incorporation of sex and gender in our work, and that these have remained largely unarticulated, misunderstood, and unaddressed in the literature. Here, we describe some of the specific challenges to the incorporation of sex and gender considerations in research involving cell cultures and laboratory animals. In our view, the main‐streaming of sex and gender considerations in basic biomedical research depends on an approach that will allow scientists to address these issues in ways that do not undermine our ability to pursue our fundamental scientific interests. To that end, we suggest a number of strategies that allow basic experimental researchers to feasibly and meaningfully take sex and gender into account in their work.—Ritz, S.A., Antle, D. M., Côté, J., Deroy, K., Fraleigh, N., Messing, K., Parent, L., St‐Pierre, J., Vaillancourt, C., Mergler, D. First steps for integrating sex and gender considerations into basic experimental biomedical research. FASEB J . 28, 4–13 (2014). www.fasebj.org
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 | 0.147 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.034 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".