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Record W2082617323 · doi:10.1177/1012690204049066

Advocacy Coalitions and Elite Sport Policy Change in Canada and the United Kingdom

2004· article· en· W2082617323 on OpenAlexaboutno aff
Mick Green, Barrie Houlihan

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
FundersLoughborough University
KeywordsEliteContext (archaeology)Political scienceGovernment (linguistics)Public relationsPublic administrationSociologyPoliticsLaw

Abstract

fetched live from OpenAlex

This paper explores the process of elite sport policy change in two sports (swimming and track and field athletics 1 ) and their respective national sport organizations (NSOs) in Canada and national governing bodies of sport (NGBs) in the United Kingdom (UK). The nature of policy change is a complex and multifaceted process and a primary aim is to identify and analyse key sources ofpolicy change through insights provided by the advocacy coalition framework (ACF). In Canada, it is evident that the preoccupation with high performance sport over the past 30 years, at federal government level, has perceptibly altered over the past two to three years. In contrast, in the UK, from the mid-1990s onwards, there has been a noticeable shift towards supporting elite sport objectives from both Conservative and Labour administrations. Most notably, the ACF throws into sharp relief the part played by the state in using its resource control to shape the context within which debates on beliefs and values within NSOs/NGBs takes place. While the ACF has proved useful in drawing attention to the notion of changing values and belief systems as a key source of policy change, as well as highlighting the need to take into account factors external to the policy subsystem under investigation, potential additions to the framework’s logic are suggested for future applications.

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 score0.803
Threshold uncertainty score0.311

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 studies0.0000.001
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 head0.058
GPT teacher head0.378
Teacher spread0.320 · 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

Citations98
Published2004
Admission routes1
Has abstractyes

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