Bibliographic record
Abstract
In 2018, the Canadian federal government created a statutory regime that would allow organizational criminal offenders avoid criminal sanctions. This was ostensibly designed as an encouragement for organizations to come forward and reveal their criminal wrongdoing. An organizational criminal offender (whether formally charged or not) can be invited by the government to negotiate what is termed a “remediation agreement”, pursuant to which any criminal charges arising from the conduct will be stayed as long as the offender abides by the terms of the remediation agreement entered into between the offender and the government. Interestingly, this regime is not available to individuals. Thus, the criminal-justice system is drawing an explicit distinction between individual offenders and their organizational counterparts. The chapter examines the procedural steps required to be taken to access this regime as well as the differences in potential outcomes that could arise when access to this regime is permitted. Thus, I begin with a description of the statutory provisions, including what constitutes an “organization” under Canadian criminal law as well as what types of offences are included (and, by extension, excluded) within the remediation agreement regime. Since the regime is so new, there are relatively few cases with respect to this regime. Nonetheless, both judicial and other commentary with respect to this regime will be analysed. Finally, I will assess whether the normative justifications for the regime are of sufficient weight to uphold it.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".