Psychiatric Diagnosis as Newly Discovered Evidence in Ireland
Bibliographic record
Abstract
This article considers how courts in Ireland have responded to newly discovered evidence that a defendant was suffering from a mental disorder at the time of the offence. Where such evidence was not known to the jury, there is a risk that a wrongful conviction may have occurred. When psychiatrists examine a defendant for the purposes of criminal proceedings, they may only have had limited time to study and diagnose the defendant. Sometimes, the defendant’s subsequent symptoms and presentation can lead to a psychiatrist revising their original diagnosis. In Ireland, a defendant can make an application arguing that this newly discovered fact shows that there has been a miscarriage of justice in relation to the original conviction. It appears that Irish courts will only accept such applications in exceptional circumstances. This article discusses the recent Court of Appeal decisions in People (DPP) v Abdi (no 2) and People (DPP) v McGinley. It analyses the reasoning of the judgments and seeks to identify what general principles can be derived from the decisions that can be used to inform future applications.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.013 |
| 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".