Regulating Unreliable Evidence: Can Evidence Rules Guide Juries and Prevent Wrongful Convictions
Bibliographic record
Abstract
Recent years have seen increasing concern over the prevalence of wrongful convictions in Canadian criminal courts. This concern is particularly pronounced in jury trials, as jurors are untrained and often lack the familiarity, experience and knowledge required to evaluate evidence of doubtful reliability. Research has suggested that three forms of evidence - eyewitness identification, confessions and jailhouse-informant testimony -pose particular reliability concerns in jury trials. The special problem, common to all three, is the tendency of jurors to overlook the factors that make them unreliable. Canadian criminal evidence law purports to address this problem, but the author argues that the law has hardened into a rigid set of category-based rules that are not particularly conducive to protecting the innocent. Rules that exclude unreliable evidence, as well as rules providing for cautionary instructions or expert testimony on its frailties, all have a place in controlling the risk of wrongful convictions. The author argues that these options should not be treated as strict alternatives. This paper begins with a discussion of the existing approach to eyewitness identification, confessions and jailhouse-informant testimony. It then offers a discussion of the two basic choices that underlie these rules, with each choice involving difficult trade-offs. The "method" choice asks whether educating a jury about the dangers of these types of unreliable prosecution evidence is preferred over limiting a jury's adjudicative freedom. The "knowledge" choice asks whether courts should allow experts to speak to the jury's misguided beliefs, or whether judges should use their own experiences in instructing and cautioning juries. The author is critical of evidentiary rules that are too rigid, and she suggests a flexible regulatory scheme for dealing with these unreliable forms of evidence. She argues that a blend of educating and limiting strategies, and of expert and judicial knowledge, will bring an effective balance that protects the innocent without unduly hindering prosecutors. The author ultimately proposes three approaches that should ground a spectrum of safeguards against the problem of evidentiary unreliability: judicial exclusion of unreliable evidence, jury education about the frailties of evidence associated with wrongful convictions, and the use of expert evidence when judicial instruction would be inadequate.
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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 | 0.106 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".