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Record W3047317205 · doi:10.1123/jsm.2019-0327

Examining Institutional Entrepreneurship in the Passage of Youth Sport Concussion Legislation

2020· article· en· W3047317205 on OpenAlexaff
Landy Di Lu, Kathryn L. Heinze

Bibliographic record

VenueJournal of Sport Management · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsLegislationLeverage (statistics)EntrepreneurshipPoliticsPublic relationsInstitutional changeQualitative researchInstitutional theoryPolitical sciencePublic administrationSociologySocial scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.298
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes1
Has abstractyes

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