Section 24(2) in the Trial Courts: An Empirical Analysis of the Legal and Non-legal Determinants of Excluding Unconstitutionally Obtained Evidence in Canada
Bibliographic record
Abstract
This empirical study explores the legal and non-legal factors influencing trial judges’ decisions to admit or exclude illegally obtained evidence under s. 24(2) of the Canadian Charter of Rights and Freedoms. Mining an original dataset of 1,472 reported decisions from 2013-2018, we found little evidence that they are affected by judges’ gender or partisan ideology. We did find, in contrast, that they are substantially influenced by judges’ previous professional background: former criminal defence lawyers are more likely to exclude than former non-criminal practitioners, who are in turn more likely to exclude than former prosecutors. We also found significant regional disparities, with judges in Quebec, British Columbia, Newfoundland, and Nova Scotia more likely to exclude than Alberta judges. The study also revealed that judges are more likely to admit evidence when trying more serious charges and less likely to do so when trying female defendants.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".