Studying professional women footballers: A reflexive commentary on being benched from recruitment
Bibliographic record
Abstract
In this reflexive commentary we provide critical reflections on the challenges of recruiting professional women football players as experienced by the researchers. We posit that the same social, systemic inequities that make continued study of women's professionalized sport so important, also generate challenges to recruiting women athletes. As we share our reflections on the difficulties we experienced throughout our recruitment process, we hope to illuminate challenges and strategies to advance sport research with professional women athletes and answer calls to amplify marginalized voices across sport studies. Namely, we identify three (inaccurate) outsider researcher assumptions that contributed to our recruitment challenges related to social, systemic inequities: (a) many professional women football players will (at some point) secure a financial sponsorship deal, (b) the football club staff would be our gatekeepers, and (c) women's football has professionalized working conditions, resources, and support. We argue that it is important to understand the challenges and gatekeepers that researchers encounter while studying professional women's sport, to address gender inequities while working towards a more socially just landscape.
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 | 0.119 | 0.344 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.040 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.042 | 0.070 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".