Bibliographic record
Abstract
This presentation will provide my personal observations regarding Forensic Science versus Pseudoscience in the Canadian legal system. I am neither a lawyer nor a judge; rather, I am a forensic scientist with over 25 years of experience in the Canadian system. My presentation focuses on relevant criteria for expert evidence considered in Canadian courts. The key ruling in R. v. Mohan (1994) provides the start of the discussion with subsequent court rulings adding various elements. In Canada, we have had several judicial inquiries, such as the Kaufmann Commission, that can serve to guide experts. Select aspects of the 2009 NAS report "Strengthening Forensic Science in the United States: A Path Forward" will also be referenced. There are some common 'criteria' often used by courts in different jurisdictions to assess expert evidence, including Forensic Acoustics. In other words, some basic expectations for all forms of expert evidence can be identified. I will attempt to show how select 'sciences' have tried to fulfill those expectations. This will involve some commentary on issues of individual examiner competency, oversight at a system level (eg. accreditation), and the need for proper and adequate method validation.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.038 | 0.049 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 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".