International Olympic Committee consensus statement: harassment and abuse (non-accidental violence) in sport
Bibliographic record
Abstract
Despite the well-recognised benefits of sport, there are also negative influences on athlete health, well-being and integrity caused by non-accidental violence through harassment and abuse. All athletes have a right to engage in 'safe sport', defined as an athletic environment that is respectful, equitable and free from all forms of non-accidental violence to athletes. Yet, these issues represent a blind spot for many sport organisations through fear of reputational damage, ignorance, silence or collusion. This consensus statement extends the 2007 IOC Consensus Statement on Sexual Harassment and Abuse in Sport, presenting additional evidence of several other types of harassment and abuse-psychological, physical and neglect. All ages and types of athletes are susceptible to these problems but science confirms that elite, disabled, child and lesbian/gay/bisexual/trans-sexual (LGBT) athletes are at highest risk, that psychological abuse is at the core of all other forms and that athletes can also be perpetrators. Harassment and abuse arise from prejudices expressed through power differences. Perpetrators use a range of interpersonal mechanisms including contact, non-contact/verbal, cyber-based, negligence, bullying and hazing. Attention is paid to the particular risks facing child athletes, athletes with a disability and LGBT athletes. Impacts on the individual athlete and the organisation are discussed. Sport stakeholders are encouraged to consider the wider social parameters of these issues, including cultures of secrecy and deference that too often facilitate abuse, rather than focusing simply on psychopathological causes. The promotion of safe sport is an urgent task and part of the broader international imperative for good governance in sport. A systematic multiagency approach to prevention is most effective, involving athletes, entourage members, sport managers, medical and therapeutic practitioners, educators and criminal justice agencies. Structural and cultural remedies, as well as practical recommendations, are suggested for sport organisations, athletes, sports medicine and allied disciplines, sport scientists and researchers. The successful prevention and eradication of abuse and harassment against athletes rests on the effectiveness of leadership by the major international and national sport organisations.
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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 | 0.063 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.028 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".