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Record W4410137477 · doi:10.1017/s0003445212009749

From Crime Policy to Victim Policy The Need for a Fundamental Policy change

2012· article· en· W4410137477 on OpenAlexaff
Ezzat A. Fattah

Bibliographic record

VenueInternational Annals of Criminology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCriminologyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

SUMMARY Recent years have witnessed great strides in applied victimology. During the 1980s legislation was passed, services were created, programs were set up, all aimed at helping crime victims and improving their unhappy lot. What is remarkable about these developments is the ease with which the legislative changes were introduced and approved. Not only was there no opposition but they were also not preceded by the usual impact studies to assess the effects they were likely to have on the CJS and the larger society. Even more surprising is that they were introduced in the absence of clear empirical avidence indicating that they do represent what crime victims really want. The paper is an attempt to show that despite the fanfare with which the new measures were introduced, they have not tangibly improved the lot of crime victims. It claims that what is necessary to achieve this goal is a new criminal justice policy, a new pénal and sentencing philosophy that places the emphasis not on punishment and retaliation but on rearation, mediation and conciliation. In most instances these two sets of goals are functionally incompatible. Parallel to this change, there needs to be another fundamental change in the traditional views on crime. The offense should cease to be regarded as an affront to the State and be viewed as an offense against the individual victim, not as a violation of an abstract law but a violation of the rights of the victim.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.450
GPT teacher head0.523
Teacher spread0.074 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2012
Admission routes1
Has abstractyes

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