Collateral Consequences, Disadvantage, and Criminal Defence Work
Bibliographic record
Abstract
Professional rules of conduct require Canadian defence lawyers to inform their clients about potential collateral consequences of criminal convictions. Drawing from qualitative interviews with 74 criminal defence lawyers, we explore issues related to both client and lawyer disadvantage and the consideration of collateral consequences in criminal courts. More specifically, we document and analyze how the “duty to inform” is experienced and negotiated by duty counsel lawyers and private counsel taking on indigent defence. We engage with scholarship on the reproduction of social inequality via criminal justice, the unique organizational realities of criminal defence, and broader questions of access to justice. We show when and how lawyers and their clients face additional burdens, which shape how collateral consequences are (a) identified, (b) brought up in court, and (c) received by prosecutors/judges. Our work highlights that challenges posed by collateral consequences cannot be overcome via criminal defence efforts alone and that current practices further exacerbate inequalities within and beyond criminal courts.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".