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Record W3120171054 · doi:10.1080/25742981.2021.1872036

Physical literacy: a sixth proposition in the Australian/Victorian Curriculum: Health and Physical Education?

2021· article· en· W3120171054 on OpenAlexaboutno aff
Trent D. Brown, Rachael Whittle

Bibliographic record

VenueCurriculum Studies in Health and Physical Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPhysical educationContext (archaeology)LiteracyPedagogyPropositionHealth literacyAustralian CurriculumSociologyPsychologyMathematics educationPolitical scienceProject commissioningHealth careGeographyPublishing

Abstract

fetched live from OpenAlex

Within the context of physical education internationally (e.g. Canada, United Kingdom, New Zealand) and the curriculum area of Health and Physical Education in Australia (and Victoria) there has been renewed interest philosophically, conceptually and practically in physical literacy. Recently, Sport Australia released the Australian Physical Literacy Framework to activate a ‘common language and consistent understanding about what physical literacy is and how it can be developed' [Sport Australia. (2020). Physical literacy. https://www.sportaus.gov.au/physical_literacy]. One context identified by Sport Australia to promote physical literacy is via schools and educators. Given that there is no explicit reference to physical literacy in the Victorian Curriculum: Health and Physical Education, our purpose is to critically examine the likelihood that physical literacy may impact curriculum, pedagogy and assessment within this jurisdiction. An intended outcome of this paper is to outline how such a concept could be enacted in the curriculum. We propose that there may be an opportunity of introducing this concept as a sixth proposition (joining educative outcomes, strengths-based approach, health literacy, critical inquiry and valuing movement).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.074
GPT teacher head0.533
Teacher spread0.458 · 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 teacher head, not a consensus.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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