Eyewitnesses in Criminal Trials: Lessons from the Miscarriage of Justice in Canada and Japan 1)
Bibliographic record
Abstract
This paper deals with witness testimony (a statement of criminal identification by an eyewitness) which is one of the most important issues to consider when we think about the problem of wrongful convictions and the miscarriage of justice in criminal trials . This issue of witness testimony concerns the fields of both psychology and law, particularly criminal law. “Building a case based on an incorrect criminal identification statement generally known as witness testimony is problematic in two ways. First, it tends to create false charges, and second, it may also result in a failure to arrest the actual criminal.”This is a quotation from a sentencing decision of the Osaka District Court in 2004. This reasoning can be applied to all cases of wrongful conviction, which result in the punishment of innocent defendants, as well as a failure to arrest the actual criminal. The question that must be asked is,“why would a witness testify incorrectly? Does this happen frequently? And are witness testimonies Public Lecture Eyewitnesses in Criminal Trials: Lessons from the Miscarriage of Justice in Canada and Japan
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".