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Record W4309463160 · doi:10.3390/ijerph192215211

The Bibliometric Analysis of Studies on Physical Literacy for a Healthy Life

2022· article· en· W4309463160 on OpenAlexaboutno aff
María Mendoza-Muñoz, Alejandro Vega-Muñoz, Jorge Carlos‐Vivas, Ángel Denche-Zamorano, José Camelo Adsuar, Armando Raimundo, Guido Salazar-Sepúlveda, Nicolás Contreras-Barraza, Nicolás Muñoz-Urtubia

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
FundersUniversidad Andrés BelloUniversidad Católica de la Santísima Concepción
KeywordsBibliometricsLiteracyCompetence (human resources)MetadataProduction (economics)VisualizationData sciencePsychologySociologyComputer scienceWorld Wide WebPedagogyArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

This article empirically provides a global overview of physical literacy, which allows for the understanding of the structure of the epistemic community studying literacy for healthy living. Publications registered in the Web of Science are analyzed using bibliometrics (spatial, productive, and relational) based on data from 391 records, published between 2007 and April 2022, applying five bibliometric laws and using VOSviewer software for data and metadata processing and visualization. In terms of results, we observe an exponential increase in scientific production in the last decade, with a concentration of scientific discussion on physical literacy in seven journals; a production distributed in 46 countries situated on the five continents, but concentrated in Canada and the United States; co-authored research networks composed of 1256 researchers but with a production concentrated of around 2% of these, and an even smaller number of authors with high production and high impact. Finally, there are four thematic blocks that, although interacting, constitute three specific knowledge production communities that have been delineated over time in relation to health and quality of life, fitness and physical competence, education, and fundamental movement skills.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0110.007
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 head0.131
GPT teacher head0.485
Teacher spread0.353 · 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 designOther design
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

Citations23
Published2022
Admission routes1
Has abstractyes

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