A Critical Management Studies Approach to Sport Management Education: Insights, Challenges and Opportunities
Bibliographic record
Abstract
Sport management education has developed in a manner consistent with conventional management education, focusing on traditional instrumental performance measures and largely ignoring wider social considerations. We endeavour to contribute to the advancement of critical approaches in sport management education. While arguing for the benefits of a sport management education that addresses the complex social issues faced in sport, we provide illustrations of critical teaching in sport management, offering examples from personal pedagogical experience. We conclude with a discussion of pragmatic issues faced in adopting a critical orientation within sport management. Our aim is to provide an approach framed by critical thought that can be used to complement existing teaching paradigms to enhance and bolster the rigour and depth of teaching in sport management.
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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 | 0.027 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".