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Record W2100483957 · doi:10.7202/017395ar

Nouvelle donne législative et causes de la criminalité « corporative »

2005· article· en· W2100483957 on OpenAlexvenueno aff
Laureen Snider

Bibliographic record

VenueCriminologie · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationCapitalismDenunciationIdeologyHegemonyPoliticsCriminal lawCapitalist stateCrime controlLegislationLawPolitical scienceCriminal justiceSociologyState (computer science)Social controlPolitical economyCriminology

Abstract

fetched live from OpenAlex

This paper examines the ideological and political collapse of laws regulating corporate crime in North America. In an era where social control and criminalization are steadily increasing, corporate crime has been normalized, shorn of its negative, criminal implications, de-regulated in law. The paper asks why this has happened, looking first at the century-long battle waged by labour and other counter-hegemonic groups to censure and control the antisocial acts of corporations through the passage of criminal legislation. Second, it examines the role criminology as a discipline played in this process, and the subsequent replacement of criminological discourse and influence by the newly-ascendent law and economics movement, which has provided the much of the academic support for de-regulation. Both developments, it is argued, are linked to changes in global capitalism and the weakened nation-state. Finally, the paper argues that the removal of regulation through criminal or administrative law, and of its accompanying rhetorics of denunciation, has grave consequences for social policy. The structural and ideological forces of global capitalism that have normalized corporate crime have also provided ideal conditions for increases in its incidence and impact.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.245
GPT teacher head0.387
Teacher spread0.142 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2005
Admission routes1
Has abstractyes

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