Advancing feminist innovation in sport studies: A transdisciplinary dialogue on gender, health and wellbeing
Bibliographic record
Abstract
Athlete health and wellbeing requires a holistic, multidimensional approach to understanding, supporting, and treating individual athletes. Building more supportive, inclusive, and equitable environments for the health and wellbeing of women and gender expansive people further requires gender-responsive approaches that promote broader cultural change. Feminist sport and exercise medicine practitioners, sports scientists, and social science researchers are increasingly coming together in their efforts to do this work. However, working across disciplines inevitably includes an array of ontological, epistemological, and political challenges. In this paper, we offer a curated 'dialogue' with a group of feminist scholars engaged in research and practice across disciplines, bringing them together to discuss some of the most pressing gendered issues in sport today (i.e., ACL injury, concussion, menstruation in sport, mental health, gender categories). In so doing, we amplify the voices of those working (empirically and clinically) at the disciplinary intersections of gender, sport and health, and learn about some of the current and future possibilities for transdisciplinary innovations and strategies for building (responsiveness to) cultural change.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".