Women Under the Sexera Harassment and
Bibliographic record
Abstract
know what to do about abuse they experience and, if they do lay a complaint, are unlikely to be satisfied with the outcomes or with the penalties for the abuser. Le harcilement sexuel et les abus sont des problkmes importants et souvent cachks aux athlites fiminines. Cet arti-cle rapporte des exemples tirks d'un sondage national rkalisk dans des kquipes canadiennes. La ripostesociale, lkgale et kthique au harcdement sexuel et aux abus dans un contexte sportif; crke un #dome de silencew Ces ripostes sont teintkesd'imp~ratijiskrieuxcomme l'hktkrosexua~isme, Le patriotisme, le nationalisme, la compktition. Ce sont les athktes intewiewkes qui ont identif;k et illustrk dans leurs propres mots, les effets spkcz~ques auxfemmes. Sexual harassment and abuse in sport is asignificant and often hidden prob-lem for female athletes. Sport re-mains a complex cultural phenom-enon and, in an effort to understand the nature and scope of the problem of sexual harassment and abuse, it has been necessary for researchers to consider "not just the athlete and her coach but also sport organizations, the police, child protection and legal agencies, other coaches, peer ath-letes, siblings and parents " (Bracken-ridge 200 1: 44). The research started with Crosset's study on male coach1 female athlete relationships and Brackenridge's (1986) article on codes of practice for coaches. By 200 1, some 26 pieces of research had been completed by 33 different re-searchers in eight countries and Womensport International had formed a Task Force of Sexual Har-assment in Sport to inform govern-ments and sport practitioners around the world. Canadian researchers such
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".