Making sense of the victim’s role in clemency decision making
Bibliographic record
Abstract
This article discusses victim engagement with the executive clemency process from a normative perspective. The authors’ aim is to explore the existing models of victim participation in clemency decision making in common law jurisdictions, in order to determine whether these possess any sound theoretical basis. The article brings together the academic literatures on victim participation and clemency functionality in order to ground the analysis. In brief, the authors' main finding is that victim involvement in clemency decision making can indeed be supported by the theoretical literature, albeit to a more limited extent than is currently practised in some common law jurisdictions. In light of the theoretical underpinnings of clemency in democratic societies and the literature on victim participation, the authors conclude by making several ‘best practice’ recommendations for future policy-making.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".