Navigating the Fine Line of Criminal Advocacy: Using Truthful Evidence to Discredit Truthful Testimony
Bibliographic record
Abstract
In Canada, lawyers are barred from using fraudulent means to mislead a court. Lawyers are also barred from permitting a witness to be presented in a false or misleading way. However, neither Canadian law nor Canadian professional codes clarify the permissibility of challenging a Crown witness with truthful evidence when defence counsel knows that the accused is guilty. This article explores the lack of guidance across the Canadian legal profession, and then uses Canadian and American legal scholarship to identify different approaches put forward on this topic. It concludes that there should not be an absolute ban on the practice of counsel for guilty accused used truthful evidence to challenge a Crown witness. Defence counsel must ensure convictions are only obtained by sufficient reliable evidence. Defense must also help clients obtain any remedy and defence not prohibited by law. However, a contextual approach should be taken in determining if the practice is appropriate and ethical in each case. This approach would consider, for example, the circumstances of the case, intended use of the evidence, legal merit to the claim it is used in support of, harm to the respective witness, and impact on justice norms such as equality, anti-discrimination and harm reduction.
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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 | 0.064 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.020 | 0.040 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".