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Record W761134033 · doi:10.1123/jsm.21.1.1

Creative Tensions and Conversations in the Academy

2007· article· en· W761134033 on OpenAlexaff
Sue Inglis

Bibliographic record

VenueJournal of Sport Management · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScrutinySociologyBalance (ability)Public relationsContent (measure theory)Service (business)Work (physics)Plan (archaeology)PsychologyPolitical scienceMarketingBusiness

Abstract

fetched live from OpenAlex

Academic life invokes creative tensions within and among teaching, research, and service. Work–life balance plays a prominent role in those tensions and in the conversations that they engender. As NASSM’s strategic plan demonstrates, sport management has grown to the point that it will benefit from closer attention to the content and potential of those conversations. Systems thinking in the scrutiny of tensions provides insight that can further inform our conversations. The resulting discourses will engage our thinking about our discipline’s values, content, and environmental influences. As a result, they will move us forward.

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)0.026
metaresearch head score (Gemma)0.048
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: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0450.076
Scholarly communication0.0370.024
Open science0.0030.030
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0070.001

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 head0.046
GPT teacher head0.360
Teacher spread0.314 · 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

Citations25
Published2007
Admission routes1
Has abstractyes

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