The Bare Supervening Necessities of Theory Development in Sport Management
Bibliographic record
Abstract
This address explores how we can find theory in new spaces and apply it to our own, unique sporting contexts. It first examines how urban regime theory can inform research on the governance of intercollegiate athletics, then discusses how and why some theories in sport management emerge and are adopted while others are not. Borrowing from Winston’s model of technological diffusion, supervening necessities are what allow some prototypes to transform into inventions; in the social sphere of sport management, they are the drivers of new concepts that are adopted and employed as theories in the field. However, Winston notes that within the social sphere are brakes that serve to slow their emergence. In turn, theories develop in sport management under similar conflicting pressures. It is these contrary forces that slow the diffusion of new ideas in the sport management field.
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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 | 0.082 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.105 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.013 |
| 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".