Psychological Predictors of Cyber Bullying in Early Adulthood
Bibliographic record
Abstract
Objectives: The aim of the present research was to investigate the possible psychological predictors of cyber bullying behavior in early adulthood. It was hypothesized that lack of empathy and emotional-behavioral problems would be related to the cyber bullying as well as will prove significant predictors of cyber bullying behavior in early adulthood. Design: It was a co-relational study and cross-sectional research design was followed. Duration and place of study: It took six months to collect data from three sites of Lahore, Pakistan; that were colleges, universities and net cafes. Sample and method: Purposive Sample of 150 young adults including 78 men and 72 women with age range 18-25 was drawn Assessment measures were Toronto Empathy Questionnaire 1 and Cyber Bullying Scale2. For emotional problems Depression, Anxiety and Stress 23 and for behavioral problems Aggression Questionnaire 4 was used. Results: Results revealed that there was a significant inverse relationship between empathy and cyber bullying, whereas significant positive relationship between emotional-behavioral problems and cyber bullying. Multiple Hierarchical regression analysis revealed that lack of empathy and emotional problems were significant predictors of cyber bullying. All the three groups including young adults from universities, colleges and net cafes perform significantly different on all study variables. The present research findings will give new directions for future studies in the field of cyber crimes as well as in making therapeutic intervention plans to treat emotionalbehavioural problems in youth.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".