Expert Evidence in Class Actions Litigation: A Proposed Framework for a Reliability Analysis
Bibliographic record
Abstract
Reliable expert evidence is critical to the adjudicative process. The Ontario Civil Justice Reform Project chaired by The Honourable Mr. Coulter Osborne, and the Inquiry into Pediatric Forensic Pathology in Ontario led by Commissioner Goudge1 each underscore the important role that expert evidence plays in judicial decision making. Reliable expert evidence is critical in class actions litigation where evidence concerning epidemiological, toxicological, and statistical evidence is often adduced to prove claims for hundreds, and in some instances, thousands, of class members. In such cases, Canadian courts are regularly faced with complex expert evidence relating to aggregated claims. Accordingly, sound decision making in class action litigation depends upon evidentiary reliability.
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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 | 0.150 | 0.203 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.024 | 0.011 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.024 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".