Investigation of Reasons of Social Media Usage of Physical Education and Sports School Students
Bibliographic record
Abstract
The aim of this research is to determine the reasons of social media usage of students studying in school of physical education and sport. The universe of the research was created 750 students from Yozgat Bozok University School of Physical Education and Sports. The sample of the study consisted of 283 students who were studying at different departments of Physical Education and Sport Department of Yozgat Bozok University in 2018-2019 academic year. Survey method was used in the study. Information on the demographic characteristics of the students was obtained by ‘personal information form. Information about determining the reasons of social media usage for students, Cemrek et al. (2014) developed by the usage of social media scale was obtained. The data were transferred to SPSS 18 software program for statistical operations. Frequency analysis, percentage analysis, arithmetic mean, t test, Anova analysis and post hoc tests were used to analyze the data.As a result of the analyzes performed; Statistically significant differences were found among the reasons of social media usage according to the education department, the purpose of using the internet, the most followed social media tool and the time variables spent on social media during the day (p<.05). There were no statistically significant differences according to gender variable (p>.05).
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".