The Predictive Power of Emotional Intelligence in Cyberbullying among Jordanian University Students
Bibliographic record
Abstract
The present study aimed to identify the predictive power of emotional intelligence in cyber violence among Jordanian university students. The researcher constructed the Cyber Violence Scale and translated the Emotional Intelligence Scale developed by Richa Jain (2015). Both scales had the appropriate indices of validity and reliability. The study sample consisted of 444 university students who participated from eight different courses to fulfill the requirements of the general college at Al-Balqa' Applied University in Jordan during the first semester of the 2016-2017 academic year. After collecting and analyzing the data, the following results emerged: The calculated mean of cyber violence among students of Al-Balqa' Applied University was 2.74 with a standard deviation of 1.32, which indicates that the level of cyber violence is in the moderate range. The overall level of emotional intelligence as well as the secondary dimensions of emotional intelligence (social skills, self-awareness, self-motivation, emotional regulation, empathy) were in the high range among students of Al-Balqa' Applied University. There was a negative correlation between the overall level of emotional intelligence dimensions as well as the secondary dimensions of emotional intelligence (self-awareness, emotional regulation, self-motivation, empathy, social skills) and cyber violence. Emotional intelligence dimensions accounted for 18% of cyber violence. Self-motivation and emotional regulation were the two most significant predictors of cyber violence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".