Sport coaches as policy actors: an investigation of the interpretation and enactment of disability and inclusion policy in swimming in Victoria Australia
Bibliographic record
Abstract
This paper draws upon education policy sociology, and sport coaching literature, in critically examining sport coaches as policy actors. Stephen Ball and colleagues’ conceptualisation of different policy actor positions and roles provided the framework for research that investigated how eight professional swimming coaches in Victoria, Australia, interpreted and enacted disability and inclusion policy. A discourse analysis of semi-structured interviews with the eight coaches reveals the complexities associated with how and why different coaches interpret and enact disability and inclusion policy imperatives in different ways in their specific club contexts. Data are presented that shows coaches adopting multiple and hybrid policy actor positions and roles as disability and inclusion policy was interpreted, translated and ultimately, expressed as pedagogic rules and practices. Our discussion brings to the fore questions about power, agency and control in coaching, while highlighting both limits and possibilities for the enactment of inclusive disability sport policies by swimming coaches working in Victoria, Australia. In conclusion we suggest that this research illustrates that coaches are capable of enacting social change, and have some agency to do so, but at the same time appear constrained by established discourses that shape policy and give important direction to pedagogic practice. We advocate that further in-depth research is required into the coaching policy-practice nexus, particularly as it relates to the advancement of equity and inclusion.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.026 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".