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Record W3111596975 · doi:10.1177/2096531120960152

Experiential Learning in a Canadian Physical Education Class: A Comparative Perspective from Pre-service PE Teachers in Canada and China

2020· article· en· W3111596975 on OpenAlexaffabout
Sydney Hector, Geri Salinitri

Bibliographic record

VenueECNU Review of Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsExperiential learningInterviewPhysical educationChinaGeneral partnershipService-learningPerspective (graphical)PedagogyPsychologyAttendanceMedical educationSociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

This article focuses on a comparative analysis of traditional Physical Education games and experiential learning on the part of teacher candidates enrolled in an Ontario Faculty of Education and one in Southwest China. Funded by the Social Sciences and Humanities Research Council of Canada (SSHRC) partnership grant for research on Reciprocal Learning between Canada and China, the researchers conducted a qualitative study interviewing ten teacher candidates from the reciprocal Faculties of Education. Emergent themes included risk-taking and resilience, willingness to implement foreign practices, division of sport-related skills, and daily physical activity (DPA). Findings suggest that DPA is overlooked in Canadian classrooms; Chinese teachers are more likely to implement foreign practices, and skills development in traditional PE games is disparate between Canada and China.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.062
Threshold uncertainty score0.447

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0270.009
Scholarly communication0.0050.002
Open science0.0020.004
Research integrity0.0010.002
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 head0.059
GPT teacher head0.467
Teacher spread0.408 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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