Bullying Predicts Reported Dating Violence and Observed Qualities in Adolescent Dating Relationships
Bibliographic record
Abstract
The relationship between reported bullying, reported dating violence, and dating relationship quality measured through couple observations was examined. Given past research demonstrating similarity between peer and dating contexts, we expected that bullying would predict negative dating experiences. Participants with dating experience (n = 585; 238 males, M(age) = 15.06) completed self-report assessments of bullying and dating violence perpetration and victimization. One month later, 44 opposite-sex dyads (M(age) = 15.19) participated in behavioral observations. In 10-min sessions, couples were asked to rank and discuss areas of relationship conflict while being video-recorded. Qualities of the relationship were later coded by trained observers. Regression analysis revealed that bullying positively predicted dating violence perpetration and victimization. Self-reported bullying also predicted observations of lower relationship support and higher withdrawal. Age and gender interactions further qualified these findings. The bullying of boys, but not girls, was significantly related to dating violence perpetration. Age interactions showed that bullying was positively predictive of dating violence perpetration and victimization for older, but not younger adolescents. Positive affect was also negatively predicted by bullying, but only for girls. These findings add to the growing body of evidence that adolescents carry forward strategies learned in the peer context to their dating relationships.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".