Including the Forgotten Party in Legal Education: Victims of Crime
Bibliographic record
Abstract
Including the Forgotten Party in Legal Education: Victims of Crime Since the 1970s, victimologists have identified victims as the ‘forgotten party’ in criminal law, emphasising its failure to recognise them as more than a witness to a crime. While victimology and our understanding of the effects of crime on victims have advanced considerably in recent years, victims largely remain the forgotten party in legal education. Law schools in Canada continue to approach victims as witnesses and fail to offer tomorrow’s legal professionals comprehensive training in victimology. Based on the premise that change starts with education, we created an interdisciplinary legal clinic for victims of crime in which law students work together with criminology students, providing legal information to victims. This article presents findings from an evaluation of the programme, which is based on a qualitative study of law students who participated in our legal clinic for victims. After a brief presentation of the programme and the training provided to students, we explore the experiences of the law students and examine how they feel their experience impacted the way they view law and prepared them to work with victims of crime.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".