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Record W2898236072 · doi:10.1136/bjsports-2018-099592

Voices of survivors: ‘you will not destroy our light’

2018· editorial· en· W2898236072 on OpenAlexafffund
Melissa D. McCradden, Michael D. Cusimano

Bibliographic record

VenueBritish Journal of Sports Medicine · 2018
Typeeditorial
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchOntario Neurotrauma Foundation
KeywordsObedienceFeelingAthletesNarrativeObligationCoachingPsychologyMedicineCriminologySocial psychologyLawPsychotherapistPolitical scienceLiteraturePhysical therapy

Abstract

fetched live from OpenAlex

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, …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.313
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations9
Published2018
Admission routes2
Has abstractyes

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