Bibliographic record
Abstract
Because of feminist activism, what were once considered incompatible entities, women and sport, have come to be united within the social fabric of the 21st century. Recent generations of women are the first to experience sport as a commonplace reality that is largely taken for granted. After initial exclusion from the first and second wave feminist agendas, many activists now recognize sport as a vehicle for the advancement of women. The female athlete has been described by some academics as a type of “stealth feminist” who can support key feminist causes without arousing a knee-jerk social response. Although female sport participation and the status of female athletes have improved significantly, the impact this has had in the lived experience of women remains to be understood. This research project seeks to conduct focus groups with female athletes to better understand their relationship with the topic of feminism and to explore the impact sport participation has had within their lives. Deeper comprehension and documentation of sport from the perspective of female participants may not only serve to help guide sport policy and programing, but may also serve to foster a united, feminist consciousness that is capable of expanding the possibilities for female athletes and for women more broadly.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".