Examination of Social Media Attitudes and Loneliness Levels of Secondary Education Students with Regard to Gender and Sport
Bibliographic record
Abstract
The purpose of the study was to examine secondary school students' attitudes towards social media and their loneliness levels in terms of gender and whether they do sports or not. The sample of the study consisted of 175 female and 269 male secondary school stıdents. To collect data personal information form, Social Media Attitude Scale (Otrar ve Argın, 2013) and UCLA Loneliness Scale (Russell, Peplau, & Cutrona, 1980) were used to in this study. Independent t test analysis was conducted to examine whether the participants' social media attitudes and loneliness levels differed with regard to gender and do sport. According to the results, there was no significant difference in social media attitudes (t = -.832, p > .05) with regard to gender. However, loneliness levels of the participants seems to be significantly different with regard to gender (t = -6.513, p = .000). When the participants were examined whether they do sports or not, there was not any significant difference in social media attitudes (t = -.427, p> .05). However, the levels of loneliness was significantly found to be different in terms of doing sports or not (t = -3.675, p = .000). Furthermore, there was not a significant relationship between social media attitudes and loneliness (p > .05). It can be concluded that sport and regular physical activity can be considered as a means to provide environments where individuals will feel far away from the feeling of loneliness and that they will feel themselves more valuable.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".