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Record W4401384936 · doi:10.1080/14927713.2024.2378801

Vertical interlocking and decision making in national and provincial/territorial non-profit sport organization boards

2024· article· en· W4401384936 on OpenAlexafffundvenueabout
Erik L. Lachance, Milena M. Parent

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of OttawaBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInterlockingBusinessProfit (economics)MarketingKnowledge managementIndustrial organizationOperations managementPublic relationsPolitical scienceComputer scienceEngineeringEconomicsMicroeconomicsMechanical engineering

Abstract

fetched live from OpenAlex

This study explains the impact of vertical interlocking on national (NSO) and provincial/territorial (P/TSOs) non-profit sport organization board decision making in a federated sport model. Data were collected from six boards in Canada (two NSOs, four P/TSOs) via 36 meeting observations, 18 semi-structured interviews, and over 900 documents. Sixty-six decisions were categorized based on vertically interlocked and non-interlocked boards (three per group). Data were analyzed via an independent sample t-test (derived from a structured observation sheet to score decisions quantitatively) and a codebook thematic analysis. Results demonstrated interlocked boards differed from their non-interlocked counterparts when making decisions regarding the length, delays, interactions, and information sources (i.e., acquisition and use). Theoretically, results provide insights into vertical interlocking’s multi-dimensional impact on decision-making constructs. Practically, NSO and P/TSO boards should perceive vertical interlocking positively, given the ability to access and use information sources not available in non-interlocked boards.

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 score0.163
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.324
Teacher spread0.308 · 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

Citations1
Published2024
Admission routes4
Has abstractyes

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