Forensic DNA Identification and Canadian Criminal Law
Bibliographic record
Abstract
See R.G. Federico, The Genetic Witness : DNA Evidence and Canada as Criminal Law (1990)33 Crim.L.Q. 204.DNA identification evidence has been extracted from items ranging from cigarette butts(e.g.R. v. McCullough(2000) , 142 C.C.C.(3d)149(Ont.C.A.) )to gum(e.g.R. v. Kyllo,［1999］B.C.J. No. 717(S.C.) (QL) )to automobile airbags(e.g.R. v. Lebeau,(1999) , 47 M.V.R.(3d)248(Ont.Sup.Ct.) (QL) ) . 94Forensic DNA Identification and Canadian Criminal Law(PENNEY) provide strong evidence of culpability, and help to exonerate the wrongfully convicted.5 DNA has become, as one Canadian judge has put it, the most dramatic forensic evidence ever discovered.6 In this paper, we describe the two statutory mechanisms in Canada governing the collection and use of DNA samples for forensic analysis : investigative warrants and DNA databank orders.Police use investigative warrants to compel criminal suspects to provide bodily samples for DNA identification analysis.DNA databank orders require convicted offenders to provide such samples so that their genetic profile may be included in a national database.We also compare these regimes to their analogues in the United States and the United Kingdom and make suggestions for reform.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".