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Record W3204622885 · doi:10.1123/jsm.2020-0399

“Sport is Double-Edged”: A Delphi Study of Spectator Sport and Population Health

2021· article· en· W3204622885 on OpenAlexaff
Brennan K. Berg, Yuhei Inoue, Matthew T. Bowers, Packianathan Chelladurai

Bibliographic record

VenueJournal of Sport Management · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsFowler Kennedy Sport Medicine Clinic
Fundersnot available
KeywordsSpectator sportSport managementPublic relationsEntertainmentDelphi methodPopulationField (mathematics)PsychologySociologyAdvertisingPolitical scienceBusiness

Abstract

fetched live from OpenAlex

The periodic examination of research agendas in sport management is necessary for the field’s advancement. In this mixed-method Delphi study, 15 leading sport management scholars forecast how the field can have a more influential voice in understanding the relationship between spectator sport and population health. Panelists agreed on the importance to not oversell or oversimplify the role of spectator sport; to improve interdisciplinary collaboration, theorization, and research design; to recognize opportunities to advance mental and social well-being; to better relate to stakeholders; and to identify distinctive health effects of spectator sport. A lack of consensus existed about the relationship between spectator sport and environmental well-being and prospects for leveraging spectator sport for participant sport. Drawing from these findings, the authors suggest that future research consider moving beyond simply measuring the effects of spectator sport on population health and, instead, assess its health effects relative to multiple forms of leisure and entertainment.

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 score0.136
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.040
GPT teacher head0.357
Teacher spread0.318 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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