Dealing with DNA Evidence in the Courtroom
Bibliographic record
Abstract
DNA has played a revolutionary role within criminal justice systems across the world. This paper, while honouring the role DNA evidence has played, nevertheless aims to set out (in plain English in order to make it readily accessible to lawyers dealing with this evidence) some on-going and new key aspects related to the use of DNA evidence in the courtroom. Areas canvassed relate to identification evidence, activity level evidence and DNA mixtures. Specific issues considered include the potential for misunderstanding of DNA statistics both generally and when ‘partial’ match profiles are involved; concerns in regard to underlying assumptions and interpretation of transfer and activity information to determine how and when the DNA was deposited; and a highlighting of a change to the way statistical calculations are made through new software being used across Australia and internationally, including ‘black box’ assumptions that go into those calculations that is particularly relevant to DNA mixtures. This article is Australian-based and some key Australian cases relevant to these issues are considered, however the issues and principles contained within the article are widely applicable within an international context.
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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 | 0.044 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".