Brazilian Gymnastics in a Crucible: A Media Data Case Study of Serial Sexual Victimization of the Brazilian Men’s Gymnastics Team
Bibliographic record
Abstract
Elite gymnastics sport culture is presently under global scrutiny. Largely ignited by the highly publicized case of serial sexual abuses in USA Gymnastics, multiple national gymnastics teams have disclosed stories of athlete abuse. Our author team utilized media data to investigate the serial sexual abuses that occurred on the Brazilian Men’s Gymnastics Team. Using media data to conceptualize athlete maltreatment is novel and facilitated our holistic interpretation of athlete maltreatment across multiple levels of athletes’ developmental systems. The authors traced the media coverage temporally and identified four overarching themes: (a) uncovering the case (subthemes—the Brazilian sport context; the Brazilian men’s gymnastics context; the club context), (b) before abuse was identified (subthemes—the coach–athlete dyad: before disclosure; the athlete: a lost childhood; social connectivity: isolation; the gymnastics system: mechanisms of abuse), (c) when abuse was recognized (subthemes—the coach–athlete dyad: athlete resistance; the athlete: identifying the impact; social connectivity: building connections; the gymnastics system: consequences of abuse), and (d) the legacy of abuse (subthemes—the coach–athlete dyad: ongoing abuses; the athlete: cyclical victimization; social connectivity: expanding connections; the gymnastics system: after abuse). Utilizing media data facilitated our culturally contextualized interpretation of athlete abuse to present tailored recommendations for practitioners.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".