Proof and Progress: Coping with the Law of Evidence in a Technological Age
Bibliographic record
Abstract
This article outlines those rules of evidence that are most likely to be called upon to fit new technologies. It identifies some of the challenges that are presented, and identifies modest techniques or suggestions for coping. Those suggestions include taking the kind of relaxed view as to when expert evidence is being offered illustrated by the Ontario Court of Appeal in R. v. Hamilton; taking a functional approach to judicial notice; ensuring that authentication and the “best evidence” rule for electronic records are not applied in a highly technical fashion; understanding the law of hearsay and remaining familiar with key hearsay exceptions; applying the law of privilege in ways that reflect the new realities that compromise privacy; understanding the limits of character evidence and the opportunities for the exclusionary discretion; and recognizing the utility in the technological presentation of evidence.
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 | 0.165 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.015 | 0.159 |
| Scholarly communication | 0.038 | 0.077 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.033 | 0.038 |
| Insufficient payload (model declined to judge) | 0.005 | 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".