In the Land of Corporate Impunity: Corporate Killing Law in the United States
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".