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Record W4384567723 · doi:10.3138/utlj-2021-0091

How victims matter: Rethinking the significance of the victim in criminal theory

2023· article· en· W4384567723 on OpenAlexvenueno aff
Leora Dahan Katz

Bibliographic record

VenueUniversity of Toronto Law Journal · 2023
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsnot available
Fundersnot available
KeywordsPunishment (psychology)Retributive justiceCriminologyCriminal lawPsychologyLaw and economicsLawSocial psychologyPolitical scienceSociologyEconomic Justice

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.207
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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