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Record W4411014869 · doi:10.1080/23750472.2025.2510309

The home team advantage: investigating household sport affinity and volunteering in sport organizations

2025· article· en· W4411014869 on OpenAlexaffabout
Le Hung Lam, Daniel Wigfield, Heather Kennedy, Ann Pegoraro

Bibliographic record

VenueManaging Sport and Leisure · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsYork UniversityUniversity of Guelph
Fundersnot available
KeywordsBusinessSport managementMarketingTeam sportPublic relationsPolitical sciencePhysical therapyAthletesMedicine

Abstract

fetched live from OpenAlex

Purpose This study examines how a household’s sport affinity influences the likelihood of volunteering in community sport organizations (CSOs), addressing the limited research on household-level determinants of sport volunteerism.Design/Methodology/Approach A cross-sectional survey design was employed, capturing a representative sample of 847 household respondents from across Canada.Findings The analysis indicates that greater household sport affinity and participation in team-based sports, as opposed to individual or less-structured activities, significantly predict household volunteerism in sport. Interestingly, conventional predictors such as household income were not found to be significant in this context.Practical Implications Practitioners should focus on targeted household recruitment through sport affinity evaluations rather than individual characteristics or coercive pressures. Using the organization’s registration platform to gather data on household sport engagement can enhance volunteer recruitment and retention efforts.Research Contribution This research more accurately reflects contemporary volunteerism in sport by examining its antecedents at the household level, rather than relying on the individual perspective that has dominated much of the existing literature across various contexts.Originality/Value By establishing a better understanding of the drivers for household volunteerism in sport, CSOs can better recruit, retain, and stabilize the voluntary workforce they rely on.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.010
GPT teacher head0.261
Teacher spread0.251 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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