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Record W4404005492 · doi:10.21275/ms241025174225

Impact of Children and Youth Sports Programs on the Development of Golf in the Country

2024· article· en· W4404005492 on OpenAlexaboutno aff
Mariia Orlova

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2024
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsYouth sportsPositive Youth DevelopmentEconomic growthPsychologyBusinessAdvertisingPolitical scienceAthletesDevelopmental psychologyMedicinePhysical therapyEconomics

Abstract

fetched live from OpenAlex

The impact of children's and youth sports programs on the development of golf in the country is a significant area of research since these programs contribute not only to the popularization of sports among the population but also to the development of physical, social, and communication skills among the younger generation. Programs aimed at involving children and adolescents become the fundamental basis for the formation of long - term interest in the sport, ensuring continuity and continuity in the development of golf at the national level. The purpose of this article is to comprehensively study the impact of such programs on the level of involvement of children and adolescents in golf, as well as to assess their contribution to the creation and development of appropriate infrastructure. The methodological approach includes the analysis of statistical data covering the dynamics of the number of participants, as well as changes in infrastructure provision in various regions of the country. Special attention is paid to the analysis of successful foreign methods, such as The First Tee programs in the USA and Future Links in Canada, which have proven their effectiveness in attracting children to golf and forming the necessary sports and social skills. The results of the study demonstrate that targeted programs aimed at working with children and adolescents contribute to a significant increase in golf engagement.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.161
GPT teacher head0.569
Teacher spread0.409 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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