Curious Judge: Judicial Notice of Facts, Independent Judicial Research, and the Impact of the Internet
Bibliographic record
Abstract
Judicial notice allows uncontroversial facts to be established without evidentiary proof. The facts must either themselves be beyond dispute because they are “notorious” (that is, generally known within the community) or they must be able to be referenced in easily accessed sources whose accuracy is beyond dispute. Judicial notice is an especially vexing topic because it goes to the heart of the epistemological inquiry of the adversarial process and the nature of the judicial function. Judicial notice implicates the allocation of responsibilities for fact finding between the parties and the court, between the judge and the jury, between the court of first instance and the appellate bodies, and between the courts and the legislature; how fact finders engage in ordinary reasoning processes to decide what a fact is; the distinction between adjudicative and legislative facts and their respective roles; and due process concerns for one or both parties. The rules governing judicially noticed facts are especially sensitive because whenever a fact is judicially noticed it is not subject to the ordinary processes for testing evidence, such as oaths and cross examination, and thus the rules implicate concerns about fairness to the parties and accuracy. For a common law precedential system, these concerns are particularly acute.Drawing on American and Commonwealth commentators, this article provides a detailed analysis of the general theory and policy of judicial notice and the role of judicial notice in the adversarial system. The article then turns to a discussion of the practice of independent judicial research and an examination of the impact of the internet on judicial notice. The article analyses the laws and policies governing judicial notice of facts and independent judicial research in Canada and the Supreme Court of Canada's legal framework. It examines independent judicial research and, most pertinently for modern practices of judicial notice, appropriate uses of internet search tools and online sources in the context of judicial notice. It considers how the internet is affecting key aspects pertaining to the judicial notice of facts: first, what “notoriety” and “community” mean; and second, what counts as an authoritative reference. The paper concludes by addressing how the internet, including search engines and online content, may affect the traditional framework for judicial notice of facts.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.022 | 0.016 |
| 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".