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Record W7005651000

Restorative Regulation of Criminality at Work in Canada: Workplace Safety, Penal Law, and Human Capability Enhancement

2020· article· en· W7005651000 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationLegislationScholarshipCriminal lawHarassmentSanctionsLabour lawEnforcementIndustrial relations
DOInot available

Abstract

fetched live from OpenAlex

From the Master and Servant legislation to the Factories Acts of the 19th century, the criminal law has always had a vital yet normatively complex role in the regulation of work relations. Even in its earliest forms, it operated both as a tool to repress collective organizations and enforce labour discipline, while policing the worst excesses of industrial capitalism. Recently, governments have begun to rediscover criminal law as a regulatory tool in a diverse set of areas related to labour law: 'modern slavery', penalizing irregular migrants, licensing regimes for labour market intermediaries, wage theft, supporting the enforcement of general labour standards, new forms of hybrid preventive orders, harassment at work, and industrial protest.This volume explores the political and regulatory dimensions of the new 'criminality at work' from a wide range of disciplinary perspectives, including labour law, immigration law, and health and safety regulations. The volume provides an overview of the regulatory terrain of 'criminality at work', exploring whether these different regulatory interventions represent politically legitimate uses of the criminal law. The book also examines whether these recent interventions constitute a new pattern of criminalization that operates in preventive mode and is based upon character and risk-based forms of culpability. The volume concludes by reflecting upon the general themes of 'criminality at work' comparatively, from Australian, Canadian, and US perspectives.Criminality at Work is a timely, rich and ambitious piece of scholarship that examines the many intersections between criminal law and work relations from a historical and contemporary vantage-point.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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 designBench or experimental
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 routes1
Has abstractyes

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