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Record W3002223613 · doi:10.1177/1356336x19899693

Pre-service teachers articulating their learning about meaningful physical education

2020· article· en· W3002223613 on OpenAlexafffundabout
Tim Fletcher, Déirdre Ní Chróinín, Mary O’Sullivan, Stephanie Beni

Bibliographic record

VenueEuropean Physical Education Review · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of CanadaIrish Research Council
KeywordsPhysical educationPedagogyArticulation (sociology)PsychologyTeacher educationQualitative researchMathematics educationProfessional developmentMeaningful learningSociology

Abstract

fetched live from OpenAlex

The purpose of this research was to examine pre-service teachers’ articulation of their learning through the development of a shared professional language of teaching practice focused on meaningful physical education. Qualitative data gathered from 90 pre-service teachers over four years in Canada and Ireland were analysed. Framed by a didactical research framework, pre-service teachers used elements of the shared language to articulate why they would promote meaningful experiences in physical education, what the features of meaningful experiences tend to consist of, and how they would use particular strategies to promote meaningful experiences. This research demonstrates how a shared language that reflects a coherent approach in physical education teacher education can support pre-service teachers to access, interpret, and articulate their learning about teaching in ways that support meaningful experiences for pupils.

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)0.012
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.098
GPT teacher head0.464
Teacher spread0.365 · 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

Citations31
Published2020
Admission routes3
Has abstractyes

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