National Paralympic sport policies influencing a country’s Paralympic success
Bibliographic record
Abstract
The Paralympics Games are increasing in competitiveness as more countries seek top medal outcomes. In response, governments are focusing on the development and implementation of effective national sport policies/systems to optimise Paralympic success. However, little is known about national sport policy influencing a country’s Paralympic success. Indeed, the literature on national elite sport policy has focused on Olympic sport and emerging Paralympic sport studies are limited to a country/sport. The aim of this research was to identify key national Paralympic sport policy interventions influencing a country’s Paralympic medal outcomes. This exploratory qualitative study was informed by a realist perspective, and by the social relational and human rights models of disability. Twenty-three semi-structured interviews were conducted with national Paralympic sport managers from the United Kingdom, Australia, France and Canada, and the data was analysed using qualitative descriptive analysis. Findings confirm that existing national Olympic sport policies are also important for Paralympic success, however, within these policies, parasport-specific processes were identified, and two policy interventions unique to Paralympic sports were found: integration of disability-specific and Paralympic sport knowledge in the sporting system, and a national framework for Paralympic athlete classification. This study advances knowledge on national Paralympic sport policies and suggests that researchers, evaluators, and practitioners need to account for Paralympic-specific policies and processes. Tailoring policies to the specificities of the Paralympic domain may provide competitive advantage in the Paralympic Games. This study argues for further research to understand how the identified policy interventions may be influenced by the country’s context.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".