Admissibility of Expert Opinions on Eyewitness Evidence: International Perspectives
Bibliographic record
Abstract
The courts of the United Kingdom and Canada have largely been averse to the reception of expert evidence that commentates upon the risks and potential deficiencies of eyewitness identification. Australian courts have trodden a somewhat unclear middle path. However, United States courts have latterly been amenable to some degree to the reception of such evidence. The decision of the Supreme Court of Pennsylvania in Commonwealth of Pennsylvania v Walker, 92 A 3d 766 (2014) is the latest (albeit with a strong dissent) to join the majority position in the United States, allowing such evidence in certain circumstances. This article reviews the bases for the different approaches adopted in the various jurisdictions and identifies areas upon which further research will be constructive in bringing about a consistent position internationally on the issue.
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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 | 0.207 | 0.315 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.031 | 0.030 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.037 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 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".