Investigation of Sports Participation Motivations of Physical Education and Sports School Students
Bibliographic record
Abstract
The purpose of this study is to determine the sport participate motivation of the students who are attending physical education and sports college and to compare them according to the demographic variables. The universe of the research is composed of 480 students who are studying different programs in Bozok University Physical Education and Sports School in 2017-2018 academic year. The sample of the research consists of 180 students who are determined using random sampling technique. As a data collection tool in the research, personal information form and Gill et al. (1983) and Oyar et al. (2001) used the Sport Participation Motivation Scale, adapted to the Turkish population. The data were transferred to the SPSS 18 package program for analysis. Frequency and percentage analyzes, t test, Anova analysis and post hoc tests were used in the statistical analysis of the data. As a result of the analyzes performed, statistically significant differences were determined between the motivations of physical education and sports college students to participate in sports according to the variables of gender, age, department of education and sports branch (p < .05). It was determined that male students have higher motivation to participate in sports than female students. Students between the ages of 18-22 have a higher motivation to participate in sports than students between the ages of 23-27. Physical education and sports teaching department students were found to have higher motivation to participate in sports than the students of the coaching education department and the sports management department. Students who are engaged in team sports have higher motivation to participate in sports than students who are engaged in individual sports.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".