Similar Fact Evidence & Crime Linkage Analysis: In Search of an Empirical Foundation to Support the Identity Inference
Bibliographic record
Abstract
In Canada, the Crown is generally prohibited from tendering evidence of the accused’s bad character in its case in chief. A significant exception is made for similar fact evidence. The Crown may tender evidence of past bad acts committed by an accused if the probative value of that evidence outweighs its prejudicial effect. The former is measured largely on the basis of the similarities and differences between the past bad acts and the particulars of the alleged offence. The Supreme Court of Canada in R v Handy, 2002 SCC 56 listed seven factors for the court’s consideration in this assessment, including “any distinctive feature(s) unifying the incidents.” Strikingly, to date, trial courts have proceeded in their application of the Handy factors without the benefit of empirical evidence on crime series patterns. This paper will explore the potential use that might be made of crime linkage analysis, particularly in cases of serial sex crimes where identity is in issue. It will describe ways in which criminal litigators might usefully engage this research either in support of the admission of similar fact evidence, or as a check against its misuse and any consequent wrongful conviction.
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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 | 0.052 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".