From Crime Policy to Victim Policy The Need for a Fundamental Policy change
Bibliographic record
Abstract
SUMMARY Recent years have witnessed great strides in applied victimology. During the 1980s legislation was passed, services were created, programs were set up, all aimed at helping crime victims and improving their unhappy lot. What is remarkable about these developments is the ease with which the legislative changes were introduced and approved. Not only was there no opposition but they were also not preceded by the usual impact studies to assess the effects they were likely to have on the CJS and the larger society. Even more surprising is that they were introduced in the absence of clear empirical avidence indicating that they do represent what crime victims really want. The paper is an attempt to show that despite the fanfare with which the new measures were introduced, they have not tangibly improved the lot of crime victims. It claims that what is necessary to achieve this goal is a new criminal justice policy, a new pénal and sentencing philosophy that places the emphasis not on punishment and retaliation but on rearation, mediation and conciliation. In most instances these two sets of goals are functionally incompatible. Parallel to this change, there needs to be another fundamental change in the traditional views on crime. The offense should cease to be regarded as an affront to the State and be viewed as an offense against the individual victim, not as a violation of an abstract law but a violation of the rights of the victim.
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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 | 0.044 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.032 | 0.036 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".