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Record W3091990544 · doi:10.1123/smej.2019-0044

Internationalizing Sport Management Programs: No Longer a Luxury, But a Necessity

2020· article· en· W3091990544 on OpenAlexaff
W. James Weese

Bibliographic record

VenueSport Management Education Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsWestern University
Fundersnot available
KeywordsInternationalizationPopularitySport managementPaceInstitutionPublic relationsScale (ratio)BusinessHigher educationMarketingField (mathematics)Political science

Abstract

fetched live from OpenAlex

Sport participation, consumption, and management are internationally focused, and the popularity of sport on an international scale shows no signs of slowing down. In fact, there is evidence that the internationalization of sport is rapidly increasing. Most North American institutions of higher learning are similarly focused and have internationalization as a high strategic priority. One could argue that sport management academic programs have not kept pace with these developments that have influenced our field and environment. While progress has been made, there is more to be done. The author chronicles the developments in the internationalization of both sport and higher education and offers eight suggestions to help sport management academicians effectively and efficiently internationalize their programs. Implementing some or all of these suggestions may better prepare graduates in their future endeavors and more effectively align sport management programs with the goals of their respective institution. Internationalization of the discipline would hold useful and practical applications for sport management students and programs.

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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.006
Scholarly communication0.0100.008
Open science0.0010.009
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0100.001

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 head0.020
GPT teacher head0.310
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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