Sustaining socially just and accurate life sciences teaching for sex, gender, and reproduction?
Bibliographic record
Abstract
Abstract For decades experts have called for improving equity in science education regarding sex, gender, and reproduction, with little large‐scale change. To identify potential approaches to change, we convened an interdisciplinary group of biologists, education researchers, and gender and science studies scholars. Our conversations revealed a fundamental need to work across multiple scales, including change within life science classes and simultaneously at larger culture and systems scales in the life sciences and society as a whole. We used the multiple‐loop learning framework to explore solutions across scales: Single‐loop learning is change within existing structures, such as addressing terminology used in teaching; double‐loop learning engages with why a problem exists, such as incorporating the history and philosophy of science into life sciences education; triple‐loop learning questions underlying assumptions, such as shifting life science's culture and norms to value interdisciplinarity; and quadruple‐loop learning involves societal‐level changes, such as working across communities and social change. We argue that cultural changes in the values and norms in the life sciences, educational institutions, and society more broadly are essential for lasting transformation.
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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".