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Record W3139493808 · doi:10.1080/00336297.2021.1898996

Towards a Decolonizing Kinesiology Ethics Model

2021· article· en· W3139493808 on OpenAlexaff
Janelle Joseph, Debra Kriger

Bibliographic record

VenueQuest · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsKinesiologySociologyAutonomyPraxisBeneficenceContext (archaeology)InjusticeEngineering ethicsEnvironmental ethicsPolitical scienceMedicineLawMedical educationPhilosophy

Abstract

fetched live from OpenAlex

Kinesiology is a discipline that relies on colonial, scientific understandings of health and the moving body. In addition, ethics courses in Kinesiology predominantly draw from Eurocentric philosophies and legal paradigms. In this article, however, the authors propose a new model of ethics that adds a greater emphasis on decolonial praxis. This process of decolonizing Kinesiology ethics requires accounting for colonial legacies in curricula and acknowledging the power relations sustained by White, patriarchal, ableist, capitalist systems. Therefore, the proposed Decolonizing Kinesiology Ethics Model (DKEM) offers six heuristics to improve ethical work in a wide range of health and sport-related careers. They are: (a) social justice; (b) practitioner vulnerability; and (c) relationships in a social-political-historical context, alongside traditional ethical principles of (d) autonomy; (e) beneficence; and (f) non-maleficence.

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)0.010
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.032
Scholarly communication0.0070.006
Open science0.0020.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.111
GPT teacher head0.411
Teacher spread0.300 · 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 designTheoretical or conceptual
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

Citations28
Published2021
Admission routes1
Has abstractyes

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