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Record W2338231128

Community-based responses to youth offending: politics, policy and practice under the Youth Criminal Justice Act

2016· dissertation· en· W2338231128 on OpenAlexaboutno aff
Lorinda Stoneman

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2016
Typedissertation
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsCriminologyPoliticsCriminal justicePolitical scienceEconomic JusticePublic administrationSociologyLaw
DOInot available

Abstract

fetched live from OpenAlex

This research focused on diversion and community-based alternatives to custody for young offenders. For the purposes of this research, diversion, and community-based responses to youth crime include informal processes and non-incarcerating sanctions utilized for young offenders for the purposes of diverting youth away from the formal justice system at any juncture, and/or reintegrating that offender within the community. Measures of interest included extrajudicial measures, extra-judicial sanctions, conferencing, restorative justice, and intensive support and supervision under the YCJA (2002). This research followed a qualitative approach to examine policy and practice. Phase 1 involved an examination of over a decade of policy-related discussions within the House of Commons and Senate as well as their respective committees and resulting legislation reported by Legisinfo. Initially, all transcripts were examined. At a later stage, a proportional stratified random sample was drawn, restricting the sample to 32 items. Phase 2 involved semi-structured interviews conducted with 14 professionals in the field of youth justice with the aim of accessing practice narratives on policy implementation. Chain-referral and maximum variation sampling techniques were employed to access a diverse group of professionals including police, youth workers, restorative justice personnel and probation officers in the regions of Greater Vancouver, the Fraser Valley and Vancouver Island in the province of British Columbia. Participants ranged in length of service from one year to over 35 years. Thematic narrative analysis of phases 1 and 2 occurred iteratively with data collection. In this dissertation, I present findings regarding community youth justice measures at three levels: the operational/practice level, the policy-making level and the macro socio-political level. Specifically, findings related to the operational level include: insufficient resources available to individual workers; narrowing the net of youth who are eligible for services; a reliance on informal and formal charitable contributions to provide basic youth justice services; and outsourcing of diversion strategies by government to community organizations. On a policy-making level, I discuss findings related to the complex fusion of restorative justice and diversion strategies; the substitution of anecdotes for evidence in policy-making; and the simple rather than complex stories used to frame the “youth justice problem” by policy-makers. Finally, on the macro socio-political level, I highlight the reversal of the welfare state and the associated implications of this reversal. I analyze and discuss the impacts that ideological and policy shifts have on policy-making and individual practice, notably on the efforts of professionals who must begin the work of closing the gaps in youth justice services, and who do so based on their own understanding of social responsibility and the “ethos of care.” This research contributes to the body of work on youth justice in Canada by exploring the connections and disconnects between policy discourses at each of the political, policy and practice levels and highlights how such a multi-dimensional analysis is a meaningful way to assess an important social policy issue.

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)0.006
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0110.006
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.089
GPT teacher head0.380
Teacher spread0.291 · 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 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
Published2016
Admission routes1
Has abstractyes

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