Voices of survivors: ‘you will not destroy our light’
Bibliographic record
Abstract
The horrors revealed in the Larry Nassar case—the US Olympic Team osteopathic doctor and team physician who sexually abused hundreds of female athletes over decades, are shocking, and it may easily be dismissed as an ‘extreme’ case. Yet it is important to note that certain elements of the Nassar situation shed critical light on the larger problem of how sport abuse can occur. To prevent future abuse, we have a moral obligation to listen to the survivors’ stories who were ignored for so long to understand how we may make sport safe for every athlete. This editorial describes narrative themes1 emerging from 150 victim impact statements heard at Nassar’s sentencing that spoke to elements of sport that facilitated the abuse. One of the most powerful norms that silenced these athletes was that in gymnastics they were taught ‘not to question authority’ and to ‘hide emotions, control [their] feelings and keep a level head’. In many sports, there has traditionally been a pervasive notion that obedience leads to success. But why? Despite the advocacy for athlete-centred or humanist coaching methods, …
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".