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Record W4390590088 · doi:10.1123/jpah.2023-0714

Physical Literacy in the Context of Climate Change: Is There a Need for Further Refinement of the Concept?

2024· article· en· W4390590088 on OpenAlexaffabout
Johannes Carl, Karim Abu‐Omar, Paquito Bernard, Julia Lohmann, Peta White, Jacqui Peters, Shannon Sahlqvist, Jiani Ma, Michael Duncan, Lisa M. Barnett

Bibliographic record

VenueJournal of Physical Activity and Health · 2024
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsContext (archaeology)LiteracyPopularityField (mathematics)Domain (mathematical analysis)Psychological interventionClimate changePsychologyCognitionPolitical scienceEcologySociologySocial psychologyGeographyPedagogy

Abstract

fetched live from OpenAlex

The concept of physical literacy (PL) has witnessed enormous popularity in recent years and has undergone substantial theoretical evolvement during the last 2 decades. However, the research field pertaining to PL has not yet initiated discussions around the challenges of climate change and the alignment with conceptualizations of planetary health. Therefore, we argue that the consideration of an "ecological domain" for individual physical activity, in the form of ecological awareness, would further evolve the concept. We illustrate how to potentially integrate adjustments within the most frequent PL definitions of the field (eg, those in Australia, Canada, England, Ireland, the United States, or by the International Physical Literacy Association) without questioning the entire integrity of these elaborate conceptualizations. An ecological domain of PL would not only interact with the postulated physical, cognitive, psychological/affective, and social domains of PL but also have important implications for the (re)design of interventions and practices in physical activity contexts. We call the scientific community, both on national and international scales, to intensify the discussions and initiate a research agenda involving an "ecological domain" of PL.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.927
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.386
Teacher spread0.338 · 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 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

Citations10
Published2024
Admission routes2
Has abstractyes

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