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Record W7027169388

Canada Needs a <i>Criminal Code</i>

2017· article· en· W7027169388 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCriminal lawCriminal justiceCriminal procedureLaw reformPublic lawTheory of criminal justiceRepealPresumptionHuman rights
DOInot available

Abstract

fetched live from OpenAlex

Contemporary criminal law is in a state of great confusion, which calls for an analysis of its foundations and practices. All those who "live the law" are questioned, from litigants to lawyers, prosecutors, judges, teachers, interveners, police officers, and legislators. Many aspects of the criminal law are covered in the Criminal Law Reform in Canada: Challenges and Possibilities.\nUnder the direction of Julie Desrosiers, Margarida Garcia, and Marie-Ève Sylvestre, this comprehensive title provides an in-depth analysis of the reform process, responsibility, procedure, penalty, administration and effects of the sentence, and restorative justice and human rights as well as questions the general principles of criminal law and liability.\nHighlights of the book Criminal Law Reform: An Idea whose Time has Come — Margarida Garcia and Richard Dubé Canada Needs a Criminal Code — Steve Coughlan Making Amendability a Priority in Criminal Code Reform — Jula Hughes What’s Good for the Goose is Good for the Gander: Considering the Merits of a Presumption of Organizational Capacity in Canadian Criminal Law — Jennifer A. Quaid The Problem of Historical Text Messages: Reasonable Expectation and Interception of Private Communications — Pierre-Luc Déziel and Alexandre Stylios Criminal law and Social Reintegration of Litigants: Obstacles and Possibilities — Mariana Raupp Repeal of the Judicial Review Act: Experiential Experience of Hope — Joanie Laganière and Joane Martel The Difficult Road to Accountability: A Study on Complaints Mechanisms to Investigate and Address Victim’s Rights Violations — Marie Manikis Restorative Justice and Criminal Restitution — Michelle S. Lawrence

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.546

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 studies0.0170.006
Scholarly communication0.0080.002
Open science0.0010.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0420.005

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 head0.027
GPT teacher head0.283
Teacher spread0.256 · 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
Published2017
Admission routes1
Has abstractyes

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