Bibliographic record
Abstract
This study aims to analyze participation motivation of the students studying in the department of Physical education and Sports teaching. The study population consists of 160 students from the Department of Physical Education and Sports Teaching in Kazım Karabekir Faculty of Education at Atatürk University in the 2019-2020 academic year. The study sample is 99 students out of 160 students by choosing the random sampling method. A form consisting of two parts has been used as a data collection tool in the study. In the first part of the data collection tool, there is a personal information form, including the students’ age, gender, and sports information branch. In the second part, participation motivation questionnaire has been used. Normal distribution criteria have been controlled using Kolmogorov-Smirnov and Shapiro-Wilk tests in the obtained data. Non-parametric tests have been used in the next stage’s statistical analysis. While comparisons of gender and sports type have been fulfilled with the Mann Whitney-U test, the Kruskal Wallis test has been used for the age variable. Consequently, it has been observed that the most crucial reason directing students to sports is “skill development” and the least important reason is “friends”. When looking at the values obtained from the Participation Motivation Questionnaire by gender, it is evident that there is no statistically significant difference according to the scores obtained by male and female students from the sub-dimensions of the scale.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".