Temporal and developmental risk factors for sexual harassment and abuse in sport. European Physical Education Review
Bibliographic record
Abstract
Recent revelations of sexual misconduct by sports coaches have challenged long-held beliefs in the educational value of sport, yet there is very little knowledge about the dynamics of sexual exploitation in sport upon which to base improvements in the practice of sports coaching or teaching. Earlier inductive research by Brackenridge (1996, 1997a, 1997b) in Britain established a set of hypothesised risk factors for sexual abuse in sport which have subsequently been reinforced by the results of survey research on elite athletes in Canada (Kirby and Greaves 1996). However, risk analysis for sexual abuse in sport has not yet been framed within a temporal or developmental sequence, nor sufficiently differentiated between elite and recreational levels of sport, or between coach-initiated and peer-initiated abuse. This paper reports selected findings from a Dutch qualitative study (Cense 1997) of 14 athletes who have survived sexual abuse in sport. The aim of the study was to identify risk factors that influence sexual abuse and harassment and to analyse which risks might be diminished through a prevention policy implemented by sport organisations. The Dutch study reinforces the earlier risk factor analyses but extends them by putting forward a preliminary temporal model of risk in sport that integrates offender behaviour with athlete and situational factors. On the basis of this model, suggestions are made to assist early diagnosis and prevention of sexual harassment and abuse by authority figures in sport. KEY WORDS: sexual abuse, sexual harassment, risk analysis 3 BIOGRAPHICAL NOTES
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".