Advocacy Coalitions and Elite Sport Policy Change in Canada and the United Kingdom
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".