Practice theory and examining and managing sport and leisure
Bibliographic record
Abstract
Rationale/Purpose This paper introduces practice theory to sport and leisure management to conceptualize how technology is integrated and changes everyday organization life, and how it can perpetuate or bring an end to unfair and discriminatory practices in sport.Design/Methodology/Approach This paper explores methodological approaches to practice theory, while also introducing several strategy-as-practice studies that could be reproduced in sport and leisure management research settings.Findings Researchers can look to the strategy-as-practice in management and organization studies to conduct future practice-based research, since it offers a way to understand an organization’s strategy and interactions between people and technology.Practical Implications A practice-theory approach allows sport and leisure managers to understand the relationships between individuals and the changing technologies that may influence how they manage across various issues, from safety of youth athletes online, the effect of big data and analytics on professional sports, and opportunities for diversity and inclusion.Research Contributions A limitation to the practice-based approach is that it is not unified, which can dissuade researchers. This paper proposes a value-added conceptual research agenda with accompanying research methods that can provide a roadmap for sport and leisure scholars to effectively use practice theory to study organizational change.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".