Method in legal-ethical reasoning, the criminal lawyer's conscience, the client and the court
Bibliographic record
Abstract
A legal-ethical dilemma occurs when two legal duties conflict or, in the alternative, when a legal duty conflicts with a moral duty. Such dilemmas are inherent in the current regime for lawyers governing legal ethics in Manitoba. The problem is how best to resolve these dilemmas. In solving this problem, the secondary literature provides the theoretical framework. Four writers exemplify four different models of the adversarial system, the lawyer-client relationship and the context of criminal defence. The framework gleaned from the secondary literature is then used to analyse the primary sources of legal ethics in the Province of Manitoba. The Manitoba Law Society Act and Code of Professional Conduct are researched to their historical origins. The current Code of Conduct is compared with not only its antecedent but also its correlatives in other Canadian jurisdictions. The conclusion reached is that legal-ethical dilemmas are best resolved by lawyers with reference to common morality. Lawyers are to be heldpublicly accountable for decisions made and results obtained in their course of representing a client. To facilitate the transition to this method in legal-ethical reasoning there will have to be legislative and regulatory changes.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".