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Record W3003031635 · doi:10.4324/9780429466991

Redesigning Physical Education

2018· book· en· W3003031635 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Redesigning Physical Education is a bold and innovative manifesto for the fundamental redesign of physical education for the twenty-first-century world. Aiming at better outcomes for children, better working conditions for teachers, and innovative solutions to the decline of school PE, it calls for a transnational collective action project based on new research frameworks, stressing the fundamental importance of health-enhancing, life-enriching active participation for all children and young people. Action-oriented and evidence-based, the book examines the key challenges driving change, including the equity agenda, institutionalization, outcome and accountability based teaching, and physical activity requirements in schools. With a truly international scope, the text explores perspectives from the USA, Canada, Scotland, England, Wales, Ireland, New Zealand, Australia, and Italy. This is important reading for students and academics studying and working in physical education, kinesiology, public health, and children's physical activity. It is also a valuable resource for pediatric exercise specialists, especially sport and exercise psychologists

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.006

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 head0.142
GPT teacher head0.543
Teacher spread0.402 · 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
GenreOther

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

Citations70
Published2018
Admission routes1
Has abstractyes

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