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Record W4414152078 · doi:10.1123/ssj.2024-0200

Jack of All Trades, Master of One: Examining Tensions Around Preparing Work-Ready Kinesiology Graduates in Canada

2025· article· en· W4414152078 on OpenAlexaffabout
Alixandra Krahn, Yuka Nakamura, Parissa Safai

Bibliographic record

VenueSociology of Sport Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsYork University
Fundersnot available
KeywordsKinesiologyEnthusiasmAppealDisciplineOptimismField (mathematics)

Abstract

fetched live from OpenAlex

This paper draws on findings from a study aimed at examining how Canadian Kinesiology programs define themselves within the post-secondary education landscape, and their students’ understandings and experiences of their studies. Using critical discourse analysis, our findings demonstrate Kinesiology’s ability to preserve its appeal among undergraduate students through its successful positioning of itself as a disciplinary field of choice and flexibility, and students’ own enthusiasm for and optimism about Kinesiology as a meaningful area for study. However, our findings also highlight Kinesiology’s entrenched alignment with narrow conceptualizations of health/healthcare, and inherent vulnerabilities in the current post-secondary education landscape given that such programs do not necessarily produce workplace-ready graduates as much as they produce ready-for-more-education graduates. We conclude by discussing the implications of such tensions.

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0400.015
Scholarly communication0.0100.002
Open science0.0030.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.204
GPT teacher head0.438
Teacher spread0.233 · 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 designQualitative
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

Citations0
Published2025
Admission routes2
Has abstractyes

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