Bibliographic record
Abstract
The five important papers in this series reflect the evolving state of research on violence and victimization. Their findings and methods underscore the importance of studying poly-victimization as the more encompassing genesis of harm across stages of development, rather than singular, isolated events. That is, children and youth who experience one type of violence are more likely than not to have experienced (or will experience) many others. Poly-victimized children become victims of further abuse and trauma and, in turn, are at increased risk of becoming perpetrators toward peers and future partners. These five papers incorporate a wider lens that is more inclusive of gender minority and ethnic minority youth, as well as underserved populations such as youth served by the juvenile justice systems (especially girls). Important developments were described in terms of recruiting difficult-to-reach populations, and ways to screen for psychological maltreatment in the background of youths. These papers demonstrate how the field is moving away from narrowly focused studies of violence/victimization, toward a more integrative, person-centered strategy. Such a strategy looks for common elements, such as healthy relationship development, that move us closer to common causes and solutions. These solutions should involve universal prevention via our education system that promotes well-being, enhances resilience, and reduces poly-victimization for all youth.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".