Eyewitnesses in Criminal Trials: Lessons from the Miscarriage of Justice in Canada and Japan 1)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it