MétaCan
Menu
Back to cohort
Record W2202482430 · doi:10.17161/jas.v0i0.4971

The Modernization of Policy-Making Processes in National Sport Organizations: A Case Study of Athletics Canada

2015· article· en· W2202482430 on OpenAlexaffabout
Mathew Dowling, James Denison, Marvin Washington

Bibliographic record

VenueJournal of Amateur Sport · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAmateurScholarshipModernization theoryPolitical sciencePublic relationsPoliticsPrioritizationPeriod (music)Policy makingPublic administrationSociologyBusiness

Abstract

fetched live from OpenAlex

This article explores the consequences of modernization on the policy-making processes of a singular National Sport Organization: Athletics Canada. In drawing upon the works of Green and Houlihan (2005) as a baseline comparison we examine how the organizations’ policy-making processes have changed over a 10-year period (2002-2012). Specifically, our analysis focuses on the nature and extent of these intra-organizational policy-related changes and how they have influenced the organizations’ decision-making capabilities. The descriptive analysis is informed by empirical data collected from eight in-depth semi-structured interviews with senior Athletics Canada personnel and concentrates on three inter-related themes (i) the development and prioritization of OTP-funded policies and programs; and (ii) the development and prioritization of evidence-based policies and programs, which, in turn, has resulted in (iii) increased inter-organizational relationship strain between Athletics Canada and its key delivery partners. More broadly, our investigation contributes to recent amateur sport scholarship that has sought to better understand how these broader socio-political shifts have influenced the specific decision-making processes of sport organizations.

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)0.002
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 score0.721
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.035
GPT teacher head0.347
Teacher spread0.312 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Amateur SportSame topicSport and Mega-Event ImpactsFrench-language works237,207