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Record W2069484363 · doi:10.1007/s11266-014-9502-x

Organizational Capacity and Organizational Ambition in Nonprofit and Voluntary Sports Clubs

2014· article· en· W2069484363 on OpenAlexaboutno aff
Anne-Line Balduck, Steffie Lucidarme, Mathieu Marlier, Annick Willem

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsDimension (graph theory)Organizational commitmentOrganization developmentTurnoverPublic relationsSample (material)BusinessPsychologyManagementPolitical science

Abstract

fetched live from OpenAlex

Abstract This research measures organizational capacity and organizational ambition in nonprofit and voluntary sports clubs. The organizational capacity dimensions of Hall et al.’s (The capacity to serve: A qualitative study of the challenges facing Canada’s nonprofit and voluntary organizations, 2003) multidimensional framework are completed with corresponding dimensions reflecting organizational ambition, and the human resources dimension is further detailed. Each dimension is adapted to be applicable in a sports clubs setting, resulting in the following organizational ambition and capacity dimensions: human resources (board, coaches, volunteers, youth coaches), accommodation, management, financing, and external orientation. Data obtained from a sample of 585 Flemish sports clubs were analyzed using exploratory factor analysis, revealing five types of sports clubs that are labeled ‘ambition,’ ‘coaches,’ ‘volunteers,’ ‘management,’ and ‘accommodation deficiency.’ The findings support the use of a multidimensional framework based on the dimensions organizational capacity and organizational ambition, and the usefulness of distinguishing among four types of volunteers.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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

Citations71
Published2014
Admission routes1
Has abstractyes

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