At Cross Purposes: The Responsible Subject, Organizational Reality and the Criminal Law
Bibliographic record
Abstract
In recent years, much attention has been directed at how best to hold business organizations criminally accountable when their operations cause large-scale disasters. Though much has been made of legal reforms allowing for proof of liability to be gleaned from a plurality of individuals or collective sources like culture, actual prosecutions still rely almost exclusively on imputation, an approach that applies very poorly to large, complex organizations. Nowhere has this been more painfully evident than in the failed efforts at accountability for the tragic train derailment in Lac-Megantic, Quebec. While many factors affect enforcement against organizations, only one is embedded in the fabric of the criminal law. It flows from the structural tension generated by applying the criminal law to organizations without clearly setting out why they merit treatment as distinct responsible subjects. This omission leaves an analytical gap that is filled, imperfectly, by human characteristics. This means imputation the easiest method of proving guilt even where a collective basis of liability exists in the law. I argue that to break with the habit of reducing collective behaviour into individual acts and intentions, we must build an organizational variant of the responsible subject that better supports an organizational locus of analysis needed to faithfully capture the nature and extent of collective wrongdoing. As I explain, this in turn would lead to better accountability in those instances, like Lac-Megantic, where collective responsibility is most desperately needed.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.068 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".