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Record W2245667628 · doi:10.1123/jsm.19.1.1

The Good, the Bad, and the Ugly: Critical Sport Management Research

2005· article· en· W2245667628 on OpenAlexaff
Wendy Frisby

Bibliographic record

VenueJournal of Sport Management · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTransformative learningSociologyRelevance (law)Context (archaeology)Power (physics)PoliticsSet (abstract data type)Critical theoryEpistemologyCritical management studiesCritical thinkingEngineering ethicsPublic relationsSocial sciencePolitical scienceComputer sciencePedagogy

Abstract

fetched live from OpenAlex

Critical social science is an underused paradigm in sport management. It can, however, help reveal the bad and ugly sides of sport, so we can uncover new ways to promote the good sides of it. The purpose of this article is to demonstrate the relevance of this paradigm for sport management teaching, practice, and research. A key assumption of the critical paradigm is that organizations are best viewed as operating in a wider cultural, economic, and political context characterized by asymmetrical power relations that are historically entrenched. Research is not neutral because the goal is to promote social change by challenging dominant ways of thinking and acting that benefit those in power. Conducting critical sport management research requires a specific skill set and adequate training is essential. Drawing on the work of Alvesson and Deetz (2000), the three tasks required to conduct critical social science are insight, critique, and transformative redefinition. These tasks are described and a number of sport-related examples are provided.

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)0.068
metaresearch head score (Gemma)0.051
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.068
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0130.093
Scholarly communication0.0240.025
Open science0.0020.009
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.377
Teacher spread0.333 · 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
GenreCommentary

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

Citations228
Published2005
Admission routes1
Has abstractyes

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