Social Sources of Research Interest in Women’s Sport Related Injuries: A Case Study of ACL Injuries
Bibliographic record
Abstract
This article offers an analysis of the social sources of biomedical interest in women’s sports injuries through a case study of anterior cruciate ligament (ACL) injuries. Although both men and women incur them, there is extensive research interest in women’s ACL injuries. Drawing on interviews with researchers who have contributed to this research, the investigation examines the social sources of this interest. Explanations lie largely in the evolution of the agenda in sport medicine to a concern with injury prevention, which coincides with a movement toward the inclusion of women in health research. The article concludes with a consideration of the political and ideological implications of the interaction of the prevention and inclusion agendas in research on women’s sport injuries. Cet article propose une analyse des sources sociales de l’intérêt biomédical pour les blessures dans les sports féminins à travers l’étude du cas des blessures au ligament croisé antérieur (LCA). Bien que les hommes et les femmes en soient tous deux victimes, il y a énormément d’intérêt en recherche pour les blessures au LCA chez les femmes. S’appuyant sur des entrevues avec des chercheurs qui ont contribué à ce projet, l’étude examine les sources sociales de cet intérêt. Les explications reposent grandement sur l’évolution de l’agenda en médecine du sport vers un souci de prévention des blessures, ce qui coïncide avec un mouvement vers l’inclusion des femmes dans la recherche sur la santé. L’article conclut par une considération des implications politiques et idéologiques de l’interaction des agendas de prévention et d’inclusion en recherche sur les blessures sportives chez les femmes.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".