Bibliographic record
Abstract
Studies conducted show that verbal and physical aggressive behaviors particularly among students are affected by the cyberbullying behaviors that are recently becoming even more prevalent with the increase in the social media tools. Such a case may also raise the need for investigating the cyberbullying behaviors of students of sports education in particular. Therefore, the aim of this study is to determine the cyberbullying behaviors of Sports College students. The sample of the study consists of 658 students (465 males and 193 females) studying at Sports Colleges in four different cities in Turkey. Among the quantitative research methods, scanning method has been used in the study. The “Cyberbullying Scale of Sports Audience” developed by Dogar and Karaca (2019) has been used in the study as data collection tool. It consists of 7 questions, which have one-dimension and 5-point Likert scale. The data obtained has had normal distribution. The student t-test has been used for dual comparisons, and one-way analysis of variance (ANOVA) with Tukey post hoc test has been used for multiple comparisons. The level of significance has been selected as p
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".