Reasons for Exoneration Among Fresh Evidence Cases in Canada
Bibliographic record
Abstract
To help understand how to correct miscarriages of justice, we analyzed the exculpatory evidence that led to exoneration among Canadian cases of wrongful conviction. Fifty-nine fresh evidence cases were identified and data about each case was collected. We examined three main characteristics of the fresh evidence, including: 1) the availability of the evidence at the time of the original trial (i.e., whether the evidence was discovered after conviction, was not disclosed at the time of trial, or whether there was a new interpretation of the evidence after conviction); 2) the typical features of the evidence (i.e., the evidence type); and 3) who was responsible for initiating the reinvestigation based on this evidence (i.e., the catalyst who brought attention to the evidence that ultimately led to exoneration). We found that in 36% of cases, exculpatory evidence existed at the time of trial, but was not disclosed to defence counsel. In addition, we found that witnesses were the primary type of exculpatory evidence, suggesting witness interviewing may be a fruitful area for investigators to concentrate their efforts. We discuss policy implications in relation to these findings, and how investigators and legal teams might use this information to help guide their reinvestigations in order to more effectively and efficiently remedy wrongful convictions.
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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 | 0.016 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".