Cross-Border Testifying Tips: U.S. Experts in Canada and Canadian Experts in the U.S.
Bibliographic record
Abstract
Professionals who are allowed by a court to serve as expert witnesses are granted the special legal status of offering opinion and theoretical evidence based on human factors research and provided facts that the expert did not witness themselves. The role of the Human Factors forensic expert in U.S. and Canadian court cases has become more common over the past two decades as lawyers become increasingly aware of the specialized nature of this field of study. U.S. and Canadian Human Factors experts sometimes find themselves being retained by firms on the other side of the border due to their specialized experience and training in a particular area relevant to the case at hand. In such situations, the expert will need to deal with differences in legal systems and differences in client expectations between the U.S. and Canada. The goal of this panel discussion session is to share the combined experience and knowledge of the panelists with the audience regarding the most significant differences between U.S. and Canadian clients, courtrooms, and procedures in forensic testimony, so that the expert knows what to expect when accepting a cross-border retention.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".