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Record W4385234574 · doi:10.1123/jsm.2022-0280

“Like Ships in the Night” and the Paradox of Distinctiveness for Sport Management: A Citation Network Analysis of Institutional Theory in Sport

2023· article· en· W4385234574 on OpenAlexaff
Mathew Dowling, Jonathan Robertson, Marvin Washington, Becca Leopkey, Dana Ellis, Andie Riches, Lee Smith

Bibliographic record

VenueJournal of Sport Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsLaurentian University
Fundersnot available
KeywordsOptimal distinctiveness theorySocial connectednessSport managementScholarshipField (mathematics)CitationSociologyBridge (graph theory)Knowledge managementPublic relationsPsychologyComputer sciencePolitical scienceSocial psychologyLibrary scienceLaw

Abstract

fetched live from OpenAlex

A central issue within sport management is the extent to which the field should develop a distinctive theoretical knowledge base. This paper empirically investigates the connectedness within (intrafield) and between (interfield) management and sport management disciplines in one specific knowledge domain—institutional theory. We utilized a database of 188 sport-related institutional studies and conducted a citation network analysis of the aggregated reference lists from these articles. We argue that the fields of management and sport management act like “ships in the night.” That is, as the field of sport management has become more distinctive, the field is becoming less connected with general management literature and contemporary theoretical discussions. Potential implications for sport management scholarship and understanding the nature of the field are discussed, along with how it may be possible (if desired) to bridge the gap between sport and management 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 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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.027
GPT teacher head0.308
Teacher spread0.281 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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