Investigating University Student Violence and the Role Islamic Education can Play to Lessen This Phenomenon
Bibliographic record
Abstract
This study aimed at investigating the causes of the university violence and the contribution of Islamic education in confronting it. The sample of the study consisted of (386) students studying at the Hashemite University during the second semester of the academic year 2012/2013. The researcher developed a questionnaire which consisted of (73) items. The results indicated that there were statistically significant differences due to gender and academic year. However, results did not show statistically significant differences due to specialization or grade average. Furthermore, the results indicated that the problem of transportation was considered as the main challenge to the university administration. The need for a transportation policy was seen urgent to reduce university violence. Finally, weak religious and moral beliefs, rigid and traditional teaching methods, and the effect of local, regional, and global media all played a major role in provoking in university violence. The results also indicated that respect for human dignity received the highest average in terms of importance and practice to reduce the phenomenon of university violence.Among the most important recommendations of the current study is working on a solution to the transportation problem and reinforcing the role of the tribe and the religious social values as well as varying and modernizing the university teaching methods and activating the role of the audio-visual and written media to keep pace with the needs of young people.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".