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Record W3030512643 · doi:10.1177/2631309x20921566

In the Land of Corporate Impunity: Corporate Killing Law in the United States

2020· article· en· W3030512643 on OpenAlexafffundabout
Steven Bittle

Bibliographic record

VenueJournal of White Collar and Corporate Crime · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsImpunityPresidencyLawPolitical sciencePoliticsCorporate lawScholarshipCorporate crimeVicarious liabilityTortCorporate governanceEconomicsLiabilityManagement

Abstract

fetched live from OpenAlex

Since the early 2000s, a number of Western capitalist states, including Australia, Canada, and the United Kingdom, have enacted criminal laws aimed at holding corporations to account for negligently killing workers or members of the public. In the United States, however, the existing respondeat superior (vicarious liability) regime remains intact. Drawing insight from semistructured interviews with corporate lawyers, nongovernmental representatives, union/labor leaders, and academics, I argue the relative impunity for corporate killing in the United States has its roots in corporate power and related beliefs in law and economics scholarship. This article documents how corporate offending is downplayed through hegemonic ideals that corporations are inherently good and law-abiding and any “bad apples” can be dealt with through existing law and market forces. In this respect, the recent rollback of various social protections is not simply a result of Trump’s presidency but instead a product of the neoliberal political, economic, and moral order.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.103
GPT teacher head0.264
Teacher spread0.161 · 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 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

Citations5
Published2020
Admission routes3
Has abstractyes

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