Recognizing the role of victim supports in building and maintaining healthy and safe communities
Bibliographic record
Abstract
The effects of crime can persist for years and can have life-long implications for some victims. The physical and emotional impact, alongside practical problems, point to the need for the rehabilitation of victims and their families in order to avoid or mitigate some of the long-term negative impacts of crime and, in so doing, contribute significantly to community well-being. The meaningful integration of assistance and supports for victims of crime into community safety strategies can contribute not only to increased public safety, but also to a host of other positive outcomes such as considerable cost savings, improvements in public health, and increased confidence in the criminal justice system. Currently, available research and metrics highlighting these linkages remain scarce, pointing to an important opportunity to strengthen the availability of data and research related to the experience of victimization and the impacts and outcomes of interventions with victims of crime. This paper explores the contribution of providing victim supports for building and maintaining healthy and safe communities, and will identify possible research directions to strengthen understanding in this area.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".