Examining Institutional Entrepreneurship in the Passage of Youth Sport Concussion Legislation
Bibliographic record
Abstract
New sport policies often prompt organizations in the field to alter their structures and processes. Little is known, however, about the tactics of those leading institutional change around sport policy. To address this gap, the authors draw on the concept of institutional entrepreneurship—the activities of actors who leverage resources to create institutional change. Using a qualitative case study approach, the authors examine how two coalitions that served as institutional entrepreneurs in Washington and Oregon created and passed the first youth sport concussion legislation in the United States. The analysis of this study reveals that these coalitions (including victims’ families, sport organizations, advocacy groups, and concussion specialists) engaged in political, technical, and cultural activities through the use of specific tactics that allowed them to harness expertise and resources and generate support for the legislation. Furthermore, the findings of this study suggest a sequencing to these activities, captured in a model of institutional entrepreneurship around sport policy.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".