Negotiated Justice and Economic Crime: Lessons from the Canadian Experience
Bibliographic record
Abstract
In this paper, I provide a Canadian perspective on the role of negotiated justice in the arsenal of enforcement responses to economic crime. I begin by delineating the legal framework within which enforcement against economic crime occurs in Canada by considering the ambit of the terms of “economic crime” and “negotiated justice” in relation to the structure of the criminal law in Canada and the role of prosecutors in the administration of criminal justice. From here, I explore how the application of negotiated justice to economic crime brings out issues that differ from those that characterize the conventional criminal justice negotiation paradigm in Canada. Finally, I turn my attention to a special framework developed to structure the negotiation and settlement of a particular class of economic crimes: the Immunity and Leniency Programs applicable to serious competition offences, in order to consider whether this framework could serve as a template for criminal justice negotiations in relation to other forms of economic crime.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.052 | 0.043 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".