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Record W3159696243

Today’s Offender, Tomorrow’s Victim: Analyzing the Connections Between Offenders and Victims

2020· article· en· W3159696243 on OpenAlexaffabout
Andie Kurjata

Bibliographic record

VenueStudent Research Proceedings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCriminal justicePresentation (obstetrics)CriminologyPsychologyEconomic JusticeSocial psychologySociologyPolitical scienceLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

This presentation examines the link between victims and offenders and how these roles are often interchangeable when it comes to a youth’s involvement in crime. Usually, this connection is disregarded because of the focus on the immediate situation and not the youth’s experiences with both roles. Because of this strong association between offenders and victims, the focus of this paper is about what stops a victim from becoming an offender and vice versa. The aspects focused on are individual and social factors, as well as the interactions and overlap between these factors. Generally, it was found that the criminal justice system and social service supports tend to only focus on individual factors while ignoring the social environmental aspects. This presentation demonstrates the importance of not only acknowledging a youth's role as both an offender and victim, but also the importance of addressing all aspects around how and why they got involved in criminal activity. By understanding the significance of these factors, this information can be used and integrated into the criminal justice system to help youth reduce their involvement in crime or to provide supports that address the cycle of victimization and offending. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Michael Gulayets Department: Sociology

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.224
GPT teacher head0.452
Teacher spread0.228 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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