Trauma-Informed Approaches to Law: Why Restorative Justice\nMust Understand Trauma and Psychological Coping
Bibliographic record
Abstract
Becoming trauma informed entails becoming more astutely aware of the ways in which people who are traumatized have their life trajectories shaped by the experience and its effects, and developing policies and practices which reflect this understanding. The idea that lawand, in particular the criminaljustice system, should be trauma informed is novel, and, as a result, quite underdeveloped. In this paper we advance the general argument that more effective, fair, intelligent, and just legal responses must work from a perspective which is trauma informed. We specifically apply this argument to legal work being carried out and developed under the rubric of restorative justice as this way of thinking about law focuses on acknowledging and repairing the harms to individuals and relationships which result from conflict, crime or other wrongdoing.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.082 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".