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Record W4399550854 · doi:10.1080/13573322.2024.2355251

Fostering intercultural dialogue: a case study of Chinese-Canadian students’ experiences and perspectives of a kinesiology program

2024· article· en· W4399550854 on OpenAlexaffabout
Moss E. Norman, Kelvin Cheng, Chunlei Lu, LeAnne Petherick

Bibliographic record

VenueSport Education and Society · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsBrock UniversityUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsKinesiologyPedagogyIntercultural communicationSociologyPsychologyMedical educationMedicine

Abstract

fetched live from OpenAlex

Recent research has drawn attention to the culture of whiteness and Eurocentrism within post-secondary kinesiology and physical education programs. By speaking with eighteen Chinese-Canadian undergraduate students, this case study examines how Eurocentrism manifests in an undergraduate kinesiology program at a western Canadian university. We found that a subtle, yet pervasive, center–margin framework was woven throughout the teaching and research culture of the program. This binary served to legitimize Western approaches to knowing and doing health and physical activity, while marginalizing – if not excluding altogether – non-Western knowledge systems. As one possible pathway for disrupting this knowledge/power hierarchy within kinesiology, we propose building opportunities for epistemic interculturalism throughout the curriculum. We maintain that a first step towards fostering intercultural opportunities is understanding the conditions of possibility that may enable or constrain non-coercive dialogical spaces. To this end, we aspire to expose the complex and subtle everyday processes through which the hegemony of the Western tradition is reproduced, which we suggest is the most significant contribution of this paper.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.089
GPT teacher head0.505
Teacher spread0.415 · 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 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

Citations2
Published2024
Admission routes2
Has abstractyes

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