A CONTEXTUAL APPROACH TO THE ADMISSIBILITY OF THE STATE'S FORENSIC SCIENCE AND MEDICAL EVIDENCE
Bibliographic record
Abstract
This article examines the admissibility of forensic science and medicine in criminal proceedings. In Part ii, we explain how reliability-based admissibility standards in the United States have been unevenly applied to expert evidence in civil and criminal cases and have not prevented wrongful convictions. In Part iii, we review a recent Consultation Paper (and report) issued by the Law Commission of England and Wales. Though focused on the need for ‘sufficiently reliable’ expert opinion evidence, we challenge its contemplation of easier admissibility for experience-based forensic sciences and techniques traditionally admitted. In Part iv we examine the evolving law on the admissibility of expert evidence in Canada. In response, we argue that while front-end reforms to the organization and practice of forensic science and medicine, advocated by the Goudge Inquiry and the American National Academy of Sciences, appear more promising than reliance on the adversary system, the gate-keeping role of trial judges should be strengthened. In the concluding section, we contend that threshold reliability standards should be grounded in criminal-justice system values, emerging empirical insights about the weakness of the adversarial trial and be sensitive to the particular evidence and its use, rather than applied mechanically using simplistic models of science and expertise.
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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 | 0.064 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.017 | 0.075 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".