The Relationship Between Exposure to Media Violence and School Bullying in Jordan
Bibliographic record
Abstract
PURPOSE: To examine the relationship between exposure to media violence and bullying among school students in Jordan.METHOD: A cross-sectional, correlational design and a self-reported questionnaire were used to answer research questions. A multistage, stratified random sampling was utilized to recruit a sample of 550 students from eight governmental educational directorates in a large governorate in Jordan. A self-reported questionnaire included demographic data, Media Violence Exposure scale, and School Bullying scale was distributed.RESULTS: Prevalence of school bullying was 47%. There was a positive correlation between media violence exposure and school bullying (r=.549); significantly more boys reported exposure to media violence, perpetrating of school bullying in general, and perpetrating of physical bullying in particular than girls (p=.00). While significantly more girls reported perpetrating of relational bullying than boys (p=.00). Media violence viewing time explained 42% of variance in school bullying scores.CONCLUSION: The findings call urgent need for intervention programs tailored by specialized health professionals to combat the consequences of this growing phenomenon.
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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 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".