Punishing Persistent OffendersExploring Community and Offender Perspectives
Bibliographic record
Abstract
Despite very diverse approaches towards punishing crime, all Western jurisdictions punish repeat offenders more harshly (a practice known as the recidivist sentencing premium). For many repeat offenders, their previous convictions have more impact on the penalty they receive than the seriousness of their current crime. Why do we punish recidivists more harshly? Some sentencing theorists argue that offenders should be punished only for the crimes they commit — not for the crimes committed and paid for in the past. From this perspective, punishing repeat offenders more severely amounts to double punishment. Having been punished once for an offence, the recidivist will pay for the crime again every time he re-offends. Is this fair? This volume explores the nature and consequences of the recidivist sentencing premium on both the theoretical and empirical levels. It begins by exploring the justifications for treating repeat offenders more harshly, and then provides examples of the practice from a number of jurisdictions including England and Wales, Canada, and the United States. Particular attention is paid to the views of two important groups: convicted offenders and the general public. If offenders believe that the recidivist sentencing premium is unjustified, they are less likely to accept the legitimacy of the justice system. As for members of the public, it is important to know whether this key element of the sentencing process is consistent with community views.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".