Bibliographic record
Abstract
Violence experienced in early and mid-adolescent romantic relationships (known as teen dating violence) is an important public health issue, and the three papers in this volume each address a different research question on this topic. Emerging research demonstrates that individuals who experience victimization in adolescence are more likely to be re-victimized in future relationships; however, past work on this topic is limited by potential confounding, and lack of assessment of potential mediators of this relationship. Thus, the first paper (Chapter Two) used data from the National Longitudinal Study of Adolescent Health to explore pathways to revictimization, adjusting for confounding using a high-dimension propensity score. Results indicated that dating violence experienced during adolescence was indirectly associated with intimate partner violence experienced 12 years later, through the experience of intimate partner violence at 5.5 year follow-up. These findings, as well as all empirical findings in the field, rest on the quality of measurement, and so the selection of a measure for a given research study is an important task. Currently, however, no comprehensive compendium exists that presents teen dating violence measures with evidence of reliability and validity and discusses strengths and limitations of these evidencebased measures. Thus, the second paper (Chapters Three and Four) presents a two-part comprehensive review of teen dating violence measures that have been the focus of psychometric testing. This review also summarizes empirical literature that uses identified measures. Due to the complex and nuanced nature of interpersonal interactions, psychological aggression is a particularly difficult construct to measure. Empirical data show that psychological aggression is common in teen dating relationships, but to more precisely answer questions about the impact of this aggression on healthy development, measures must be designed that capture psychological aggression that is purposeful, serious and perceived as harmful. The third and final paper in this volume (Chapter Five) reports on the initial adaptation of a measure of severe psychological aggression (the Measure of Psychologically Abusive Behaviors; Follingstad, 2011, Journal of Interpersonal Violence, 26(6)) for teen dating relationships. Together, these three papers advance understanding of teen dating violence and support its developmental and public health importance.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".