Bibliographic record
Abstract
In this case note, the authors review a recent decision of the Supreme Court of Canada in which the Court tightened the admissibility requirements of expert witnesses. The Supreme Court confirmed that expert witnesses owe a special duty of impartiality at common law to provide ‘fair, objective and non-partisan assistance’ to the trier of fact. On the question of admissibility, the Supreme Court adopted an approach broadly consistent with England by imposing a threshold eligibility requirement for the admission of expert evidence. An expert will be qualified to testify only if he or she is aware of and willing to carry out their duty of impartiality to the court. In addition, the trier of fact continues to act as a gatekeeper by assessing the probative value of the proposed expert's evidence and weighing it against the potential for prejudice. Only where the probative value exceeds the potential for prejudice will it be admitted. If the evidence of an expert witness is admitted, less serious concerns about the impartiality of the expert witness can inform the weight accorded to the evidence. The authors conclude that the Supreme Court's decision is a welcome development in Canadian law as it establishes a clear test designed to better safeguard the integrity of the trial process.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".