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Record W4414561569 · doi:10.1080/23750472.2025.2561975

Managers’ perceptions, attitudes, and roles in fostering inclusion and addressing exclusion in competitive sport for youth from low socioeconomic backgrounds

2025· article· en· W4414561569 on OpenAlexaffabout
Alexandro Allison-Abaunza, Andrea Woodburn

Bibliographic record

VenueManaging Sport and Leisure · 2025
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsInclusion (mineral)Socioeconomic statusCompetitive sportPerspective (graphical)Social exclusionCompetition (biology)

Abstract

fetched live from OpenAlex

Purpose/Rationale To explore the roles and decision-making processes of competitive sport program managers related to the inclusion and exclusion of youth from low socioeconomic backgrounds.Methods This qualitative research employed purposeful sampling to interview 11 Sport-études program managers from 11 different sports. The study, conducted through semi-structured interviews, delves into the perspectives of these managers, emphasizing the exploration of perceptions, attitudes, and roles regarding social inclusion within the context of amateur sport, with a focus on Bourdieu's concepts of habitus, capital, and fields.Findings Program managers’ dispositions toward accessibility issues in competitive sports programs highlight their substantial decision-making powers, which are tempered by external factors such as government regulations and sports federations. The findings underscore how institutional coherence inadvertently contributes to higher participation fees, reinforcing the status quo, and reveal well-intentioned cost-reduction strategies that may perpetuate exclusion.Practical Implications In Canada, where both federal and provincial governments determine sports policies, the interaction between these levels creates a complex environment with conflicting priorities, such as sport performance versus accessibility. Provinces have significant influence over local policies. Understanding how funding and program recognition impact resource allocation is crucial for managers navigating these complex challenges and promoting effective policy outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.305
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2025
Admission routes2
Has abstractyes

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