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Record W2990460841 · doi:10.1123/jsm.2019-0054

Analyzing Collaborations Involving Nonprofit Youth Sport Organizations: A Resource-Dependency Perspective

2019· article· en· W2990460841 on OpenAlexaff
Gareth J. Jones, Katie Misener, Per G. Svensson, Elizabeth Taylor, Moonsup Hyun

Bibliographic record

VenueJournal of Sport Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResource dependence theoryResource (disambiguation)Dependency (UML)Transactional leadershipPublic relationsPerspective (graphical)Context (archaeology)Nonprofit organizationNonprofit sectorBusinessKnowledge managementValue (mathematics)Service (business)SociologyMarketingManagementPolitical science

Abstract

fetched live from OpenAlex

Interorganizational relationships are a well-established practice among nonprofit youth sport organizations seeking to acquire key resources and improve service efficiencies. However, less is known about how broader trends in the nonprofit sector influence their utilization. Guided by Austin’s collaborative continuum and resource dependency theory, this study analyzed how interorganizational relationships are utilized by different nonprofit youth sport organizations in one American context. The results indicate that high-resource organizations primarily utilize philanthropic and transactional forms of collaboration, whereas integrative collaboration is more likely among low-resource organizations. The discussion draws on resource dependency theory to provide theoretical insight into this association, as well as the implications for collaborative value generated through interorganizational relationships.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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