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Record W2963381883 · doi:10.5539/jel.v8n4p227

An Examination of the Relationship Between Strength and Speed in Elementary School Students

2019· article· en· W2963381883 on OpenAlexvenueno aff
Yasin Arslan, Serdar Aktan

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSprintPearson product-moment correlation coefficientMathematicsLong jumpPsychologyStatisticsTournamentJumpPhysical therapyMedicinePhysicsCombinatorics

Abstract

fetched live from OpenAlex

This study intends to examine the relationship between strength and speed in Cumhuriyet Elementary School in Samsun. The study group consists of 240 students (120 boys and 120 girls) aged 11–14 years who participated voluntarily in the study through random selection. This study investigated the relationship among the values of leg and back strength, 30-second sit-up, standing long jump, vertical jump, reaction time, and 10 m and 20 m sprint running of 5th through 8th graders. Intragroup strength and speed relationships of each class and gender were examined separately in the study. Data were analyzed through Pearson product-moment correlation coefficient in SPSS package program with a .05 margin of error (p < .05). As a result of the study, it was observed that the strength and speed increased in conjunction with the age variable and there was a significant negative correlation between 10 m and 20 m sprint running and strength values. That is, the sprint running time decreased as the strength increased. According to the results obtained in the study, it was seen that there was a significant relationship between strength and speed in school children aged 11–14 years and that speed and strength performances affected each other. However, no significant linear relationship was found between reaction time and strength.

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)0.001
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 score0.152
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.062
GPT teacher head0.354
Teacher spread0.291 · 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
Published2019
Admission routes1
Has abstractyes

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