THE DEFENCE COUNSELS ETHICS IN PLEA BARGAINING:LOSING SIGHT OF THE INNOCENT?
Bibliographic record
Abstract
The vast majority of accused who appear before a criminal court in Canada will not proceed to trial and most of those will plead guilty to some offence. This means that a substantial portion of a defence lawyer’s cases will be resolved. Sometimes this will occur after months or years of negotiation, sometimes on the court-house steps, but all will involve some form of discussion between Crown and defence. These negotiations have commonly been referred to, by the public and participants in the criminal justice system alike, as "plea bargaining". Plea bargaining is now an accepted and integral part of our criminal justice system. The process involves an exchange of information between Crown counsel and defence counsel about the strengths and weaknesses of their respective cases and the circumstances of the offence and of the offender. Experienced Crown and defence counsel use this opportunity to ensure that individual justice is done. Through this process, an accused will surrender his right to trial, with its accompanying procedural safeguards, in exchange for concessions aimed at sentence reduction and certainty. For some, the term plea bargaining implies that justice is a commodity that can be bought, sold and bartered and thus negative connotations have resulted. It also inaccurately assumes that plea bargaining relates solely to agreements concerning guilty pleas. Discussions between counsels frequently include a vast array of considerations, much more than negotiated guilty pleas, and sometimes do not, in fact, result in guilty pleas at all . Whether this practice is a blight or a blessing on the criminal justice system has been much debated . Due to its strong focus on efficiency and its resemblance to an "assembly-line conveyor belt" , plea bargaining can be linked to what the American scholar Herbert Packer defined as a crime control model of justice whereby "the criminal justice process is controlled by prosecutors, with the primary aim being a stream-lined guilty plea". The defence counsel’s role is nonetheless very important in ensuring that the innocent accused does not get "caught" in what could be seen as a criminal factory, especially if the accused decides to "cut their losses" and plead guilty. In this way, defence counsel has a duty to protect the innocent accused’s rights and circumvent this incremental descent into poor judgment, not forgetting the image of the criminal justice system itself. What is the defence counsel’s ethics in this process? The main focus of this essay will be on the ethical considerations for defence counsel when engaging in plea bargaining, in the subset of resolution discussions, the negotiated guilty plea, while keeping in mind the risk of wrongful conviction. This essay will show that, to the exception of the Canadian Bar Association Model Code of Professional Conduct, there is little guidance on ethics in the plea bargaining process.
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".