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Record W2059894417 · doi:10.1080/01924036.2003.9678699

Bill C‐7: The new youth criminal justice act: A darker young offenders act?

2003· article· en· W2059894417 on OpenAlexaffabout
Chris Giles, Margaret Jackson

Bibliographic record

VenueInternational Journal of Comparative and Applied Criminal Justice · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPunitive damagesLegislationSanctionsPolitical scienceCLARITYEconomic JusticeCriminologyCriminal justiceGovernment (linguistics)LawSociology

Abstract

fetched live from OpenAlex

Recently, in Canada, there was a push to replace existing youth legislation, entitled the Young Offenders Act (YOA). This push was a result of several perceived failures of the YOA and has culminated in a new piece of youth legislation, Bill C‐7: The Youth Criminal Justice Act (YCJA). The Department of Justice has stressed that the YCJA will rectify the substantive problems of the YOA. This policy paper provides an analysis of the claims of the Canadian government by focusing on the substantive sections of the YCJA, comparing the YCJA with the YOA, and incorporating social science research dealing with the probable effects of the innovations. Specifically, this analysis focuses on the use of extrajudicial sanctions, adult transfers and the decreased emphasis on due process rights in the YCJA. The analysis shows that the YCJA potentially embodies a much more punitive model of youth justice in Canada, evidenced by its focus on the protection of society. Two of the major deficiencies of both acts are that they lack clarity in their respective Declarations of Principle due to a mixed model of justice and the insufficient use (probable in the case of the YCJA) of informal court measures. Lastly, this analysis uncovers that aside from the increased potential punitiveness of the YCJA, there is very little substantive difference between the YOA and YCJA. Thus, the YCJA is likely to suffer from the same deficiencies as the YOA.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.184
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.078
GPT teacher head0.361
Teacher spread0.283 · 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

Citations6
Published2003
Admission routes2
Has abstractyes

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