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Record W2054842219 · doi:10.3390/socsci3030326

Child Protection in Sport: Reflections on Thirty Years of Science and Activism

2014· article· en· W2054842219 on OpenAlexaboutno aff
Celia Brackenridge, Daniel Rhind

Bibliographic record

VenueSocial Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsSafeguardingChild protectionPolitical scienceNeglectPublic relationsChild abuseCriminologyPoison controlSociologySuicide preventionPsychologyMedicineLawEnvironmental healthPsychiatryNursing

Abstract

fetched live from OpenAlex

This paper examines the responses of state and third sector agencies to the emergence of child abuse in sport since the mid-1980s. As with other social institutions such as the church, health and education, sport has both initiated its own child protection interventions and also responded to wider social and political influences. Sport has exemplified many of the changes identified in the brief for this special issue, such as the widening of definitional focus, increasing geographic scope and broadening of concerns to encompass health and welfare. The child protection agenda in sport was initially driven by sexual abuse scandals and has since embraced a range of additional harms to children, such as physical and psychological abuse, neglect and damaging hazing (initiation) rituals. Whereas in the 1990s, only a few sport organisations acknowledged or addressed child abuse and protection (notably, UK, Canada and Australia), there has since been rapid growth in interest in the issue internationally, with many agencies now taking an active role in prevention work. These agencies adopt different foci related to their overall mission and may be characterised broadly as sport-specific (focussing on abuse prevention in sport), children’s rights organisations (focussing on child protection around sport events) and humanitarian organisations (focussing on child development and protection through sport). This article examines how these differences in organisational focus lead to very different child protection approaches and “solutions”. It critiques the scientific approaches used thus far to inform activism and policy changes and ends by considering future challenges for athlete safeguarding and welfare.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.398
Teacher spread0.343 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations87
Published2014
Admission routes1
Has abstractyes

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