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Record W2803959361 · doi:10.5430/wje.v8n3p12

Participation Motivation in University Students Who Engage in Different Team Sports

2018· article· en· W2803959361 on OpenAlexvenueno aff
Ümit Doğan Üstün

Bibliographic record

VenueWorld Journal of Education · 2018
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMultivariate analysis of variancePhysical educationTest (biology)Medical educationSample (material)Statistical analysisApplied psychologyMathematics educationMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to examine the motives for university students who engage in different team sportsmutually. The cross-sectional method was used in the study. The study sample consisted of 280 university studentsfrom Dumlupinar University School of Physical Education and Sports. The participants were chosen according tostratified random sampling method and participated in the study voluntarily. In the study in order to collect dataTurkish version of Gill and colleagues’ Sports Participation Questionnaire was used. In the evaluation of the data inaddition to descriptive statistical methods, MANOVA was used as the hypothesis test. According to MANOVAresults, there were significant differences between the motives for university students in achievement, physicalfitness, skills development and movement/being active factors. The findings of this study can assist intercollegiatecoaches and athletic administrators to understand the motivational patterns of university students for participating insports and allow them to develop strategies which can prevent students from quitting sports participation andexercise.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.342
Teacher spread0.312 · 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.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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