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Record W4285323318 · doi:10.17509/tegar.v5i1.41072

Physical Literacy Assessment of Elementary School Children in Indonesian Urban Areas

2021· article· en· W4285323318 on OpenAlexaboutno aff
Andi Suntoda, Anira Anira, Wildan Alfian Nugroho, Ricky Wibowo

Bibliographic record

VenueTEGAR Journal of Teaching Physical Education in Elementary School · 2021
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsIndonesianCompetence (human resources)Physical educationLiteracyPsychologyDescriptive statisticsMathematics educationData collectionStatistical analysisMedical educationPedagogyMedicineStatisticsSocial psychologyMathematics

Abstract

fetched live from OpenAlex

Currently, physical literacy recognizes as a critical part of physical education. The purpose of this study was to measure Physical Literacy Elementary School Children in Indonesian Urban Areas, a total of 60 students (23 girls, 38.3%; 37 boys, 61.7%) grade 5 (aged 11-12 years) as a participant for survey studies. The instrument used to measure PL is the Canadian Assessment of Physical Literacy (CAPL). It's used survey research methods. The analysis technique in this study uses descriptive statistical analysis to investigate the results of students' physical literacy based on the data that has been collected—technical data analysis using SPSS software. The result showed that students have good motivation and confidence. However, the level of knowledge and understanding of data is still relatively low, as well as the physical competence.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.470
Teacher spread0.451 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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