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Record W4285803799 · doi:10.3390/socsci11070310

Evaluation of Publicly Accessible Child Protection in Sport Education and Reporting Initiatives

2022· article· en· W4285803799 on OpenAlexaff
Ellen MacPherson, Anthony Battaglia, Gretchen Kerr, Sophie Wensel, Sarah McGee, Aalaya Milne, Francesca M. Principe, Erin Willson

Bibliographic record

VenueSocial Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHarmAthletesContext (archaeology)Inclusion (mineral)Public relationsPsychologyDiversity (politics)Equity (law)Medical educationPolitical scienceMedicineSocial psychology

Abstract

fetched live from OpenAlex

Despite sport being a vehicle through which youth may achieve positive developmental outcomes, maltreatment in the youth sport context remains a significant concern. With increased athlete advocacy and research demonstrating the high prevalence of maltreatment in sport, and the urgent need to address it, many international organisations have created child protection in sport initiatives. Of particular focus to athletes and researchers is the provision of evidence-based comprehensive education and independent reporting mechanisms for athletes who experience harm. The current study examined the extent to which the publicly accessible information provided by three sport-specific child protection organisations regarding education and reporting is aligned with recommendations provided by researchers and athletes. With regard to education, the findings highlight accessibility, programming for various stakeholders, and coverage of topics of interest (e.g., forms of harm and reporting processes). However, educational information about equity, diversity, and inclusion and information on how to foster positive environments in sport was lacking. For reporting mechanisms, results showed that each organisation’s approach to receiving reports of maltreatment varied, including their ability to directly intake, investigate, and sanction instances of maltreatment. The findings are interpreted and critiqued considering previous literature and recommendations for future research and practice are suggested.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.135
GPT teacher head0.455
Teacher spread0.320 · 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

Citations22
Published2022
Admission routes1
Has abstractyes

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