MétaCan
Menu
Back to cohort
Record W2344517525 · doi:10.1177/026975800401100204

Victims' Perspectives on Restorative Justice: How Much Involvement Are Victims Looking For?

2004· article· en· W2344517525 on OpenAlexaff
Jo-Anne Wemmers, Katie Cyr

Bibliographic record

VenueInternational Review of Victimology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRestorative justiceMediationCriminologyCriminal justiceHarmEconomic JusticeTheory of criminal justiceVictimologyProcedural justicePolitical scienceRetributive justiceSociologyLawPoison controlPsychologySuicide preventionMedicineMedical emergency

Abstract

fetched live from OpenAlex

There is considerable debate among both academics and professionals about whether restorative justice offers victims a better deal than the traditional criminal justice system. Advocates of restorative justice argue that restorative justice, which focuses on reparation of the harm suffered by the victim, is undoubtedly better equipped to respond to victims' needs than contemporary criminal justice systems. Failure by victims' advocates to embrace restorative justice has been criticised as a disservice to victims. However, victims' rights advocates are wary of the possibility that restorative justice programs are insensitive to the needs of crime victims and that they will place an additional burden on victims. Who is right? Do victims want to participate in the criminal justice process and if so, how? As Fattah (2001) suggests, the only way to resolve this debate is to ask crime victims. In this paper we present data from a study of victims of crime who were invited to participate in victim—offender mediation. In addition, procedural justice theory, which offers a theoretical framework for understanding the role that victims prefer to play in criminal justice procedures, will be presented. The paper closes with recommendations for a victim-oriented approach to criminal justice.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.035
GPT teacher head0.384
Teacher spread0.349 · 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 designQualitative
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

Citations58
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueInternational Review of VictimologySame topicCriminal Justice and Corrections AnalysisFrench-language works237,207