Unveiling sextortion in sport: a global inquiry into the nexus of sexual violence, abuse of power, and corruption for enhanced safeguarding
Bibliographic record
Abstract
Abstract Sextortion, a distinct form of sexual misconduct intersects with both sexual violence and corruption. Within the sphere of sport, marked by inherent power differentials and hierarchical structures, cases of sexual abuse and corruption persist, with sextortion emerging as a concerning manifestation of these pervasive issues. While sextortion shares commonalities with other forms of sexual abuse, such as harassment and assault, a distinguishing feature lies in how coercion is leveraged through authority or power imbalances. Unlike more overt forms of abuse, sextortion often involves subtle or implicit threats, where compliance is sought in exchange for perceived privileges or opportunities within the sporting environment. Leveraging Institutional Theory and Applied Ethics, this study aims to provide a comprehensive exploration of sextortion in sport. Despite increasing awareness, research on sextortion in sport remains limited. Previous studies lack data specific to this elusive misconduct, primarily relying on empirical data related to sexual abuse. This study represents the first empirical investigation into sextortion in sport, drawing on data collected from 49 countries and endeavours to quantifiably communicate the scale of sextortion. Through data analysis of over 500 elite athletes, community sport practitioners, and sport industry professionals aged 17 and above, the research sheds light on experiences related to abuses of entrusted power for sexual gain. Results found 20% (n = 96) of global respondents experienced sextortion, including 37 minors at the time of the incident, encompassing diverse genders, abilities, and identities. Sextortion was identified across 41 of the 49 surveyed nationalities and within 19 of 26 sport categories from grassroots to elite levels. This research deepens the understanding of sport-related sextortion and underscores the importance of addressing this pervasive issue through further theoretical and empirical inquiry. It emphasises the critical role of good governance, clear safeguarding protocols, increased awareness of power dynamics, consent, and the importance of diverse regional data in effectively combating sextortion in the sporting domain.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".