Electronic Records and the Law of Evidence in Canada: The Uniform Electronic Evidence Act Twelve Years Later
Bibliographic record
Abstract
This article analyzes the adequacy of The Uniform Electronic Evidence Act, twelve years after its adoption, in dealing with the complexity of the records created, used, or stored in the digital environment. In the face of rapidly changing technology, the authors believe that the nature and characteristics of electronic records cannot be accounted for by simple modifications to the existing law of evidence, but require a new enactment following upon a close collaboration among records professions, legal and law enforcement professions, and the information technology profession. The new rules, comprehensively encompassing issues of relevance, admissibility, and weight of electronic documentary evidence, must be based on the body of knowledge of each profession, on the findings of interdisciplinary research, and on existing records-related standards. The enactment of such rules would help the courts make accurate findings of fact, based on electronic records that are created in a reliable environment and preserved in an authentic form for as long as they might be needed, and would alleviate ongoing confusion about the admissibility and use of electronic records in litigation.
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 | 0.039 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".