Admissibility Compared: The Reception of Incriminating Expert Evidence (I.E., Forensic Science) in Four Adversarial Jurisdictions
Bibliographic record
Abstract
LR162 Our study surveys and summarizes the leading decisions rather than a detailed empirical study of actual case practices across jurisdictions.Both would be interesting and informative, but this offers a first attempt to survey leading decisions against formal rules and overarching criminal justice objectives and values.3 See generally FED.R. EVID.; FED.R. EVID.702 (Federal Rule of Evidence concerning expert testimony); Australian Evidence Act 1995 (Cth) (a statutory scheme covering everything from cross examination to admission of evidence). 4 It should be noted, however, that there are many differences, not all of which should be considered trivial.Canada, for example, has fewer trials before juries than the other jurisdictions.Many prosecutors and judges in the United States are elected, and the United States retains civil juries, making the admissibility of expert opinion evidence an important, and frequently controversial, issue in civil proceedings (e.g.tort and product liability litigation).There are no capital cases or capital juries in England, Canada, and Australia.Australia and England tend to provide relatively well-resourced defense lawyers and are more likely to expend state resources on defense experts than most U.S. states.Undoubtedly, these and a myriad of other differences in practice, traditions, and resourcing (of courts, police and forensic sciences, as well as parties) influence the ways in which forensic science and medicine evidence is developed, contested, and admitted. 5 While there can be quite significant differences in actual practices, many of the techniques feature remarkably similar ingredients across our sample.Many of these similarities flow from information and technology sharing or the use of proprietary systems.
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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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".