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Record W3201405705 · doi:10.35993/ijitl.v7i1.1494

Deweyan Progressive Education within Ontario Elementary Health and Physical Education

2021· article· en· W3201405705 on OpenAlexaffabout
Prof. Dr. Thomas Ryan, Daniel T. Ryan

Bibliographic record

VenueInternational Journal of Innovation in Teaching and Learning (IJITL) · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsNipissing University
Fundersnot available
KeywordsProgressivismPhysical educationHealth educationMental healthGovernment (linguistics)ConstructiveProgressive educationCurriculumPedagogyPsychologyMedical educationMedicinePublic healthPolitical scienceNursingPolitics

Abstract

fetched live from OpenAlex

The objective is to explore Deweyan Progressive Education within Ontario Health and Physical Education. The need to review this area was instigated within the last two years as the Ontario provincial government in Canada has implemented new 2019 Ontario Health and Physical Education curricular guide which contains significant modernizations. The document established a concern for mental health development, online safety, bullying prevention, road safety, substance abuse, concussions, and healthy body image within the 250-page document. The authors undertook a latent content analysis revealing a challenge to compress this curricular content into Health and Physical Education classes that are infrequently scheduled. Teachers, it is understood, will learn that students need progressive instruction and constructive feedback as they practise, reflect, and learn experientially in a safe environment. This review supports educators as they work to better understand the term progressive education and its current pertinence. Keywords: Dewey, philosophy, progressivism, health instruction, 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)0.003
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.338
Threshold uncertainty score0.679

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.008
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.479
Teacher spread0.435 · 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

Citations2
Published2021
Admission routes2
Has abstractyes

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