Bibliographic record
Abstract
The amount of bullying escalating in schools in recent years has been approached as an issue that needs to be investigated, whose causes need to be searched and for which preventive efforts should be made. This study is aimed at examining the bullying tendencies among preadolescents. The study was carried out with middle school preadolescents in Konya between the years 2016 and 2017. The voluntary participants were 122 female and 78 male students with an age range of 13 to 14 years. The participants were administered the Bullying Tendency Scale, which was developed and whose validity and reliability was established by Dölek (2002), and the demographic information about the students were collected using a Personal Information Form developed by the researcher. Analysis of the data obtained in the study revealed significant results in favour of male students in negative reflection, lack of emotional sharing, causing distress, use of force and bullying tendency according to the gender of participants. In terms of age factor, significant results were obtained in favour of 14 year-olds in causing distress, use of force and bullying tendency. For negative reflection, no significant difference was found in justification and lack of emotional sharing according to the age of preadolescents. Similarly, no significant difference was found in relation to the socio-economic status of the participants. Recommendations were offered based on the results of the study.
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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 | 0.002 |
| 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) | 0.001 | 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".