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Record W2614332202 · doi:10.71781/2021

Le système de justice pénale pour adolescents et les droits internationaux de l’enfant : obligations du Canada et jeunes racialisés

2016· dissertation· fr· W2614332202 on OpenAlexaboutno aff
Azinatya Caron-Paquin

Bibliographic record

VenueOpen MIND · 2016
Typedissertation
Languagefr
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsnot available
FundersUniversity of California, Davis
KeywordsPolitical scienceHumanitiesSociologyPhilosophy

Abstract

fetched live from OpenAlex

La justice criminelle devrait être adaptée aux mineurs et répondre à leurs besoins spécifiques selon le droit international des droits de l’enfant. Or, ce mémoire démontre que les droits internationaux de l’enfant compris dans les traités et autres instruments de droit international ne sont pas respectés au Canada. Le non-respect des droits de l’enfant en matière de justice juvénile se traduit par une violation des protections internationales fondamentales contre la discrimination raciale. Afin d’étudier les répercussions de la violation des droits du mineur dans la justice criminelle sur les jeunes racialisés, l’auteure adopte un cadre théorique critique de la race. La loi canadienne sur le système de justice pénale (LSJPA) est évaluée à la lumière des instruments internationaux de protection des droits de la personne selon quatre thèmes, soit (1) l’accent de la justice juvénile canadienne mis sur la répression, (2) l’accès entravé aux mesures et sanctions extrajudiciaires, (3) l’emploi abusif du placement sous garde ainsi que (4) l’assujettissement à une peine adulte. Chacun de ces quatre thèmes aborde la question de la discrimination raciale telle que vécue par les Autochtones et jeunes d’appartenance aux minorités visibles.

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)0.004
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: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.005
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.032
GPT teacher head0.352
Teacher spread0.320 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueOpen MINDSame topicTorture, Ethics, and LawFrench-language works237,207