Bibliographic record
Abstract
Recent reviews by peak scientific bodies have concluded that forensic bitemark identification is not a demonstrably valid science. In the United States, the practice of forensic bitemark identification has been linked to at least 14 wrongful convictions and has been the subject of considerable academic study. Much less is known about the use of forensic bitemark identification in Canadian courts. To remedy this lack of knowledge, we performed an exhaustive search of the reported Canadian case law. We found 14 cases in which courts relied on a forensic bitemark identification, a number that likely underestimates the use of this practice. Still, in the cases we found, forensic bitemark experts overstated the accuracy and reliability of their practice, and did not appear to disclose the considerable controversy in the field. Furthermore, and despite repeated directions from the Supreme Court of Canada that trial judges should exercise a robust gatekeeper role in the face of invalid science, none of the courts excluded bite mark analysis, nor expressly questioned the scientific validity of the practice. We discuss these findings and provide recommendations based on the principle of transparency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".