Bibliographic record
Abstract
Canadian courts draw a tenuous distinction between expert scientific evidence and what they characterize as specialized knowledge gained through the expert witness’s experience, training, and research. This characterization is based on unclear criteria and has significant consequences. Notably, specialized knowledge regularly receives considerably less scrutiny than that which is characterized as science, while still often serving as powerful inculpatory evidence in criminal trials. Moreover, specialized knowledge is often provided by figures that carry an air of authority, like police officers and scientists. This article focuses on the leading opinion on specialized knowledge, the Court of Appeal for Ontario’s decision in R v Abbey. An analysis of Abbey’s application to three fields of contested specialized knowledge (including the evidence the Abbey Court admitted, but fresh evidence revealed as fundamentally unreliable) provides two general insights. First, while Abbey could be interpreted as providing for a flexible and probing analysis of all expert evidence, courts have often relied on it to justify giving almost no scrutiny to specialized knowledge. Second, this review of the post-Abbey jurisprudence suggests that scrutiny focused on the transparency of the expert’s data and analysis, and whether that analysis can reliably be applied to the relevant factual question, may provide a valuable way to evaluate expertise.
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 | 0.115 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.028 | 0.025 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 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".