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Record W2787263902

Exploring University-Based Physical Literacy Programming: Perspectives of Service Providers.

2018· article· en· W2787263902 on OpenAlexaffabout
Christopher Borduas, Erin Cameron, Kyoung June Yi

Bibliographic record

VenueRevue phénEPS / PHEnex Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsOperationalizationConceptualizationConstruct (python library)Transparency (behavior)Thematic analysisPsychologyMedical educationSociologyPedagogyPublic relationsQualitative researchComputer sciencePolitical scienceMedicineSocial science
DOInot available

Abstract

fetched live from OpenAlex

Physical literacy (PL) has become a prominent concept in education and sport, particularly within Canada. While the term PL has been used for two decades, many researchers still operationalize the construct differently. Objective: The purpose of this study was to explore the experiences of those leading university-based PL programs in Canada. The objectives in this study were to gain insights into the: (1) approaches used for delivering PL programs; and (2) strengths/challenges of delivering university-based PL programs. Methods: Participants who were directly involved in PL programming at the post-secondary level were recruited for semi-structured one-on-one interviews. Data were analyzed using thematic analysis. Results/Discussion: Two main concepts were discovered: conceptualization and implementation. It became clear that the ideological origins of those developing and leading the programs played a crucial role in how PL is conceptualized. Lack of transparency in the theoretical underpinnings of PL has lead to differences in PL programs.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.095
GPT teacher head0.352
Teacher spread0.256 · 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.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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