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Record W2933184355 · doi:10.1080/14927713.2019.1582356

Claims of positive youth development: a content analysis of mixed martial arts gyms’ websites

2019· article· en· W2933184355 on OpenAlexaffvenueabout
Theresa Beesley, Jessica Fraser‐Thomas

Bibliographic record

VenueLeisure/Loisir · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsYork UniversityMcGill University
Fundersnot available
KeywordsPsychologyContent analysisMartial artsCategorizationPositive Youth DevelopmentCoding (social sciences)FacilitationThe artsMedical educationApplied psychologyDevelopmental psychologyMedicineSociologyComputer scienceArtVisual arts

Abstract

fetched live from OpenAlex

Mixed martial arts (MMA) is a newly established and rapidly growing sport. Given questions surrounding claims of developmental outcomes for youth through MMA broadcasted on gym websites, coupled with parents’ practices to gather and utilize website information, it is important to explore the content of MMA gym websites. This study describes the content of MMA gyms’ youth programs’ websites, examining proposed developmental outcome claims for youth and processes of facilitation. Using a quantitative content analysis of 18 MMA youth gym websites in Toronto, Canada, a 37-item coding manual was developed to categorize content. MMA gyms drew upon popular media, anecdotal evidence and selective academic research findings to promote the benefits of their youth programs to parents. The claims of developmental outcomes and facilitation of positive youth development appear misguided and raise concerns for parents seeking information on child MMA programs. Findings highlight potential implications for parents interested in enrolling their children in MMA.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.055
GPT teacher head0.285
Teacher spread0.231 · 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 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

Citations4
Published2019
Admission routes3
Has abstractyes

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