Subjecting the corporation to criminal sanctions : a review of the issues
Bibliographic record
Abstract
This thesis reviews some of the issues concerning the criminal liability and sanctioning of corporations and individuals involved in corporate crime. Prohibitions against conspiracies to lessen competition and illegal mergers under the Combines Investigation Act are used for illustrative purposes. The nature of these offences and the goals which they are designed to achieve, from an economic and political point of view, are discussed. The limitations of the criminal law and the criminal justice system, as presented by the Law Reform Commission of Canada and Professor Packer, are used to evaluate the appropriateness of the criminal law and the criminal justice system for enforcing prohibitions against conspiracies to lessen competition and illegal mergers. It is concluded that the system is appropriate for enforcing the laws against conspiracies to lessen competition and inappropriate for regulating mergers. The corporate entity is the most common vehicle through which conspiracies to lessen competition takes place. The nature of the corporation, how it makes and implements decisions, and its relationship to individuals within the corporate structure are examined in order to shed some light on how corporate behavior can be controlled. The present methods used to attach criminal liability to corporations and an alternative method, structural liability, are discussed. The liability of individuals involved in corporate crime through aiding or acquiescing, is also considered. There is a discussion of some of the rules peculiar to corporations. The goals which judges hope to achieve in sentencing corporations for illegal conspiracies and the appropriate criminal sanctions to be used to achieve compliance from corporations and individuals involved in corporate crime are considered. A number of recommendations are made with regard to improving the control of corporate behavior through the criminal justice system.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
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".