Advocacy and the Innocent Client
Bibliographic record
Abstract
Much of our knowledge about wrongful convictions is derived from known exonerations, which typically involve serious violent offences and lengthy sentences. These represent only a small proportion of offences prosecuted in Canada each year, and little is known about how often innocent defendants may be wrongfully convicted of less serious offences. Recent discussions have begun to focus on the problem of false guilty pleas, in which defendants choose to plead guilty to a lesser offence to avoid the time and cost required to defend their innocence. The majority of our knowledge of the factors contributing to wrongful convictions is based on American scholarship, with less empirical research exploring wrongful convictions within the Canadian context. The present research surveyed Canadian criminal defence lawyers about their experiences representing innocent clients, including their perspective on the underlying causes of wrongful convictions in Canada and their recommendations for reform to the criminal justice system. Nearly two-thirds of defence counsel in this study reported that they had represented at least one client who was convicted despite credible claims of innocence. Many reported that they regularly see innocent clients choose to enter a strategic false guilty plea, perceiving no meaningful or realistic alternative. Counsel described a system designed to elicit a guilty plea, with lengthy pre-trial delays, routine denial of bail, inadequate funding of Legal Aid, costly defence options, padded charges, and false evidence ploys. This research expands our knowledge of wrongful convictions in Canada, their hidden prevalence, and systemic problems that increase the likelihood of their occurrence.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.023 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".