Dating Violence Through the Lens of Adolescent Romantic Relationships
Bibliographic record
Abstract
The articles in the focus section of this issue center on a particular form of relationship violence that occurs during a particularly challenging developmental period: adolescence. Although large-scale surveys have documented the prevalence of abuse in teen dating relationships (i.e. more than 25% of male and female high school students report having experienced some form of physical abuse in a dating relationship; Foshee 1996; OKeefe 1997) it often escapes attention or concern. Presumably the laissez-faire attitude that has long existed toward many of the struggles of adolescence reflects the dismissive way we often treat teens attempts at finding romantic love. Although many of us have overlooked its developmental significance until very recently researchers and practitioners who have an interest in child maltreatment have the least difficulty grasping the importance of dating violence in the transmission of violence and abuse across the life span. Violence and abuse toward an intimate partner is arguably the most common form of violence in society (Wolfe Wekerle & Scott 1997). Broadly defined it encompasses any attempt to control or dominate another person physically sexually or psychologically resulting in harm. However how do violence and abuse develop? Are they so common that they should be considered developmentally normal or are they connected to important individual family and cultural experiences that can be addressed earlier on? These are some of the critical questions raised by these provocative articles which we would like to emphasize in our commentary. (excerpt)
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".