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Record W2794596098 · doi:10.5539/ies.v11n4p126

Influence of Motor Activity on the Development of Voluntary Attention in Children Aged 6-7 Years in Rural and Urban Micro-Community

2018· article· en· W2794596098 on OpenAlexvenueno aff
Larisa М. Zakharova, Victoria S. Zakharova

Bibliographic record

VenueInternational Education Studies · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Training Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTurnoverVoluntary associationDevelopmental psychologyRural areaPhysical activityMotor skillChild developmentPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The article is devoted to the problem of the influence of physical activity on the development of voluntary attention in children aged 5-7, which largely determines the success of schooling. The studies conducted in different countries prove the existence of such a correlation, which, at the same time, is not always unambiguous. We studied the nature of this correlation in the children of rural and urban kindergarten. The results of the pilot study showed that there were no significant differences in the development of the basic indicators of voluntary attention among the rural children attending various sports clubs and those who do not go in for sports, in contrast to the children attending kindergarten in the city. The influence of the family on the children’s desire to lead an active lifestyle has been also determined.

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 score0.017
Threshold uncertainty score0.035

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 communication0.0000.000
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 head0.079
GPT teacher head0.462
Teacher spread0.382 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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