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Record W4391697153 · doi:10.1123/kr.2024-0005

Physical Education Teacher Education: The Past, Present, and Future Questions

2024· article· en· W4391697153 on OpenAlexaff
Matthew D. Curtner‐Smith, Tim Fletcher

Bibliographic record

VenueKinesiology Review · 2024
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsBrock University
Fundersnot available
KeywordsScholarshipPhysical educationSocializationTeacher educationListing (finance)PedagogyPsychologySociologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

The purpose of this article is to overview the history of research in physical education teacher education (PETE), discuss contemporary trends, and identify future directions for scholarship and teacher education practice. Teacher education is defined as formal and informal experiences that contribute to teachers’ education across their careers. Using the phases of occupational socialization and Kosnik and Beck’s “seven priorities of teacher education” to frame an analysis of literature from the 1980s through to the present, a brief summary of research on PETE is provided, using the chronological categories of past and present. The analysis takes into account implications for PETE that were left by the global pandemic, where traditional PETE practices were significantly disrupted by a shift to online learning. The chapter is concluded by listing questions regarding PETE that researchers and teacher educators might tackle in the future.

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)0.013
metaresearch head score (Gemma)0.013
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: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0040.009
Scholarly communication0.0080.018
Open science0.0010.003
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0060.001

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 head0.065
GPT teacher head0.511
Teacher spread0.446 · 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
GenreReview

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

Citations12
Published2024
Admission routes1
Has abstractyes

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