Teen perceptions of adolescent dating violence
Bibliographic record
Abstract
INTRODUCTION: Previous research shows that adolescents who experience dating violence most often disclose their victimization to a peer or friend, more so than to other sources of support. However, surprisingly little research has explored how adolescents respond to peer disclosures of dating violence. Addressing this gap, the present study assessed variations in adolescents' perceptions of blame, interpretations of the incident as violence, and intentions to respond across physical, psychological, sexual, cyber-psychological, and cyber-sexual dating violence scenarios. METHODS: As part of a national research project across Canada, 663 high school adolescents (432 girls, 65.2%) between the ages of 14-17 were randomly assigned to complete a questionnaire which included one of five different hypothetical dating violence scenarios. Next, participants responded to questions about their perceptions of the incident, as well as victim and perpetrator blame and responsibility, and their intentions to respond. RESULTS: Results indicated that the type of dating violence experienced and the age and gender of participants all played a role in perceptions of blame, understandings of violence, and intentions to respond. CONCLUSIONS: As one of the first studies to explore how adolescents perceived and responded to dating violence, considering both in-person and cyber forms of dating violence, this study fills an important gap in the literature. Findings underscore the uniqueness of cyber forms of dating violence and how pre/intervention programs must address the specific contexts and issues unique to each type of dating 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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".