Bibliographic record
Abstract
This chapter explores the more general and arguably distinct ethical obligations of those who practice criminal law either as defence lawyers or prosecutors. As you read this chapter, you should ask yourself whether you are satisfied with the justifications offered for the ethical rules that we have carved out for defence lawyers and prosecutors in this context and whether you think we have achieved the right balance. To assist you in thinking about the modern-day ethical roles of criminal lawyers, we begin with two historical cases, one from England and the other from Quebec, which frame the obligations in arguably extreme terms – defence lawyers justified in unbridled zealousness within the bounds of the law on the one hand, and the prosecutor as a “minister of justice” on the other. In reading the descriptions of the cases consider whether you think the duties of defence lawyers and prosecutors should be so different. What would justify that sort of difference? What common framing for the duties of defence lawyers and prosecutors might be available?
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".