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Record W2790626213 · doi:10.1123/jsm.2017-0233

“You Can’t Just Start and Expect It to Work”: An Investigation of Strategic Capacity Building in Community Sport Organizations

2018· article· en· W2790626213 on OpenAlexaff
Patti Millar, Alison Doherty

Bibliographic record

VenueJournal of Sport Management · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsWestern UniversityUniversity of Windsor
Fundersnot available
KeywordsCapacity buildingWork (physics)Process (computing)Context (archaeology)BusinessPublic relationsKnowledge managementProcess managementComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Capacity building is a targeted approach to addressing organizational challenges by focusing development efforts on specific needs. Utilizing Millar and Doherty’s process model of capacity building, the purpose of this study was to (a) gain insight into the nature of the conditions and processes of capacity building in the community sport context and (b) examine the veracity of the proposed model. Interviews were conducted with organizational members from two community sport organizations that were purposefully chosen and happened to have introduced new programs: one that experienced successful capacity building that enhanced program and service delivery and one that experienced unsuccessful capacity building where organizational needs were not effectively addressed. Findings revealed that the thoroughness of the needs assessment, the selection of appropriate capacity building strategies, and readiness to build capacity were key factors in the (lack of) success of the capacity building efforts. Implications for practice and future research on organizational capacity building 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 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)0.002
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 score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.088
GPT teacher head0.322
Teacher spread0.234 · 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

Citations35
Published2018
Admission routes1
Has abstractyes

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