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Record W2050653856 · doi:10.1123/jsm.2014-0296

Social Media Scholarship in Sport Management Research: A Critical Review

2015· review· en· W2050653856 on OpenAlexaff
Gashaw Abeza, Norm O’Reilly, Benoît Séguin, Ornella Nzindukiyimana

Bibliographic record

VenueJournal of Sport Management · 2015
Typereview
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsScholarshipSocial mediaSociologyDisciplinePublic relationsSport managementSocial scienceWork (physics)Cross disciplinaryPolitical scienceData scienceEngineering

Abstract

fetched live from OpenAlex

This work critically assesses the history and current state of social media scholarship in sport management research. Methodologically, the study is based on a comprehensive census review of the current body of literature in the area of social media. The review identifies 123 social media articles in sport management research that were mined from a cross-disciplinary examination of 29 scholarly journals from January 2008 (earliest found) to June 2014. The work identifies the topic areas, the platforms, the theories, and the research methods that have received the (most/least) attention of the social media research community, and provides suggestions for future research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0170.014
Science and technology studies0.0020.003
Scholarly communication0.0050.007
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.423
GPT teacher head0.515
Teacher spread0.092 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreReview

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

Citations143
Published2015
Admission routes1
Has abstractyes

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