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Record W4254242263 · doi:10.1017/9780511762116.009

Victimology

2014· book-chapter· en· W4254242263 on OpenAlexaff
Jan van Dijk, Jo-Anne Wemmers

Bibliographic record

VenueCambridge University Press eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCulpabilityBlameCriminologyCausationPsychologyCriminal justiceVictimologySubject (documents)Political scienceSocial psychologyLawPoison controlSuicide preventionChild abuseMedicine

Abstract

fetched live from OpenAlex

INTRODUCTION In modern times there was in Western countries almost no place for the victims of crime in criminal justice. The victim had become the forgotten third party of the criminal trial. Likewise criminologists exclusively focused their studies on offenders and largely ignored the role and problems of victims. The first publications on victims dealt with the role of victims in the causation of crimes. The focus of these studies was on the degree of guilt of the victims for the crime committed against them (e.g., provocative behavior). This early interest in the culpability of victims has later been critiqued as manifestations of “victim blaming.” The inclination to blame victims of serious crimes for their misfortune has itself been the subject of social psychological studies. Experiments by Lerner and others have revealed that victims of serious crimes often elicit negative responses from their environment because their situation poses an acute threat to the fundamental belief in a just world. By assuming that the victim bears some responsibility for his or her victimization through irresponsible behavior, others can reassure themselves that they have nothing to fear for themselves. Around 1970 grassroots organizations discovered the needs of victims of domestic and sexual violence and began to provide services for them such as shelter homes and rape crisis centers. Around the same time some criminologists took up an interest in the situation of crime victims as a special research topic. Victimization surveys revealed not only that many victimizations were never reported to the police (the dark numbers of crime) but also that many reporting victims were dissatisfied with the way their cases were handled by the authorities. A sizeable minority of reporting victims even complained that they had been retraumatized by their treatment by the police and the criminal justice system (secondary victimization). Soon an international movement came into being, lobbying for improved services for victims and for the introduction of victims’ rights in criminal procedure (Walklate, 2007; Wemmers, 2003).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0040.009
Scholarly communication0.0060.007
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0330.009

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes1
Has abstractyes

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