Seeking Justice by Plea: The Prosecutor’s Ethical Obligations During Plea Bargaining
Bibliographic record
Abstract
Canadian Crown prosecutors enjoy tremendous discretionary power. They can leverage this power during plea bargaining by structuring the terms of plea deals and by engaging in aggressive negotiation tactics, thereby exerting a disproportionate influence on plea bargaining processes and outcomes. This article considers how Crowns should wield their power to shape plea bargains in light of their ethical obligation to seek justice. In particular, it considers how Crowns should identify the just case outcomes they will pursue through plea bargaining and assesses which bargaining strategies they should employ or eschew in pursuit of those outcomes. In the process, the article addresses a few especially thorny questions, including: whether Crowns should ever strategically overcharge defendants to facilitate plea negotiations; how Crowns ought to balance the accuracy of criminal charges against the fairness of criminal sentences when the two are in tension; and how Crowns can strike an appropriate balance between plea bargaining fairness and efficient case management. The article offers several concrete policy recommendations aimed at helping Crowns satisfy their ethical obligation to seek justice in the context of plea bargaining.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".