MétaCan
Menu
Back to cohort
Record W2148339188 · doi:10.1177/0899764013509892

Toward a Multidimensional Framework of Capacity in Community Sport Clubs

2013· article· en· W2148339188 on OpenAlexafffundabout
Alison Doherty, Katie Misener, Graham Cuskelly

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of WaterlooWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecreationClubPublic relationsContext (archaeology)Sport managementBusinessProfessional sportCommunity organizationMarketingPolitical science

Abstract

fetched live from OpenAlex

Community sport clubs are a type of membership association largely run by member volunteers who organize and deliver opportunities for recreational and competitive sport participation. These clubs are where people are most likely to engage in organized sport, and have become a focus for achieving social policy objectives. It is important to understand the structures and processes that enable these organizations to meet their member-focused mandates. The purpose of this study was to develop a framework of organizational capacity in this context by uncovering critical elements within multiple capacity dimensions, namely, human resources, finance, infrastructure, planning and development, and external relationships. Focus groups with presidents of 51 sport clubs across Ontario revealed key strengths and challenges that impact the ability of these organizations to achieve their sport delivery goals. Variation by club size was observed. Implications for practice and future research on community sport clubs and membership associations are presented.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.004
Science and technology studies0.0050.025
Scholarly communication0.0100.009
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.278
Teacher spread0.230 · 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 designTheoretical or conceptual
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

Citations192
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueNonprofit and Voluntary Sector QuarterlySame topicNonprofit Sector and VolunteeringFrench-language works237,207