Taking Notice of Equality: Judicial Notice and Expert Evidence in Trials Involving Equality Seeking Groups
Bibliographic record
Abstract
This article deals with various ways that the law of evidence in Canada and in other common law jurisdictions can be changed so as to facilitate access to justice for equality-seeking groups. In particular, the author deals with the doctrine of judicial notice and the admission of social science evidence (esp. as this relates to Aboriginal peoples). The author suggests that the scope of judicial notice should be expanded, and that the onus and burden of proof should distributed among the parties having regard for the equality implications for impecunious equality-seeking groups. The author also discusses the way in which the burden on these groups of introducing social science evidence can be relieved, and suggests alternatives for evaluating this evidence with an eye to equality.
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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 | 0.458 | 0.693 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.054 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.061 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 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".