GAITKEEPING IN CANADA: MIS-STEPS IN ASSESSING THE RELIABILITY OF EXPERT TESTIMONY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Incriminating expert testimony is a leading cause of wrongful convictions. Academic commentators and authoritative scientific research bodies agree that forensic comparison techniques such as fingerprint identification, toolmark comparison and bite mark analysis should be subjected to validation studies and experts subjected to proficiency testing prior to expert evidence being admitted in criminal trials. Canadian case law on the admissibility of expert testimony increasingly emphasizes demonstrable reliability as a condition of admission. In this article, we critically assess the BC courts’ approach to reliability in R v Aitken. In R v Aitken, “forensic gait analysis” was offered for the first time in a Canadian courtroom. We suggest that the growing judicial attention to reliability is heartening, but that Canadian judges and lawyers have not yet developed the tools necessary to conduct a sound assessment of the reliability of incriminating expert testimony. The authors draw on authoritative research and policy reports to offer suggestions about how to improve the Canadian judicial approach to assessing reliability.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it