How victims matter: Rethinking the significance of the victim in criminal theory
Bibliographic record
Abstract
Classic theories of punishment have been deeply criticized for their failure to attribute significance to victims and the fact of their victimization within their proposed frameworks for the justification and distribution of punishment. Retributive theory, in particular, has been criticized for its failure to recognize the significance of victims. Some theorists have been led by this lacuna to adopt a victim-centred approach to punishment as a solution to the ‘absence-of-victim’ problem. Per victim-centred approaches, the very justification and imposition of punishment rely on victims and the restoration of egalitarian relations between the offender and victim that were disturbed by crime. This move toward constructing criminal law and punishment in terms of victim recognition and vindication has become increasingly popular and has been endorsed by a number of prominent theorists, yet it raises important worries. This article proposes an alternative solution, embracing the insight that victims ought to matter to the punishment of those who offend against them, yet without constructing the edifice of criminal law and punishment on the function of victim vindication. It offers an account of the way in which the introduction of a victim changes the balance of reasons in favour of punishment, becoming important to the determination of whether or not punishment ought to be imposed (in full). Yet it does so without taking the ‘victim’s turn’ – that is, without reconstructing the institution of criminal punishment entirely in victim-centric terms.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.129 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".