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Record W2166906191 · doi:10.7202/1025771ar

A Descriptive Profile of Physical Education Teachers and Programs in Atlantic Canada

2014· article· en· W2166906191 on OpenAlexaffvenueabout
Lynn Randall, Daniel B. Robinson, Tim Fletcher

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsSt. Francis Xavier UniversityBrock UniversityUniversity of New Brunswick
Fundersnot available
KeywordsPhysical educationSubject (documents)Quality (philosophy)Subject matterPsychologyDescriptive statisticsMathematics educationDescriptive researchDancePedagogyMedical educationSociologyCurriculumMedicineSocial scienceLibrary scienceComputer scienceVisual arts

Abstract

fetched live from OpenAlex

The purpose of this research was to investigate the extent to which quality physical education is currently being taught in Atlantic Canada. We used survey methods to generate descriptive data indicating: (a) the backgrounds of those who teach physical education and (b) what is being taught in physical education. Our findings suggest physical education is taught by a group of mostly-white teachers with varying qualifications, interests, and experiences in teaching the subject. Further, sport experiences tend to dominate the subject matter that students engage with, at the expense of dance and gymnastics. Although some physical education programs could arguably be classified as being of a sound quality according to the national subject association, we contend that more needs to be done to present the subject as a diverse enterprise, both in terms of who teaches and what is taught in physical education.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.392
GPT teacher head0.488
Teacher spread0.096 · 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 designObservational
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

Citations14
Published2014
Admission routes3
Has abstractyes

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