Electronic Records as Documentary Evidence
Bibliographic record
Abstract
The new electronic record provisions that are now part of almost all of the Evidence Acts in Canada are as important as any statutory law or common law concerning the use of records as evidence. They bring six important improvements to the evidentiary law of business records. It is argued, however, that their most serious defects are that they: (1) perpetuate the best evidence rule — a rule rendered redundant by electronic records and information management (RIM); (2) do not deal with hearsay issues; (3) do not cure the defects of the business record provisions in regard to electronic records; and (4) unnecessarily complicate the law. But these defects can be substantially lessened by judicial interpretation that accomplishes what the business records provisions should have accomplished. Although a topic left to a future article, this article should be read with the assumption that the electronic record provisions are interdependent with: (1) the new electronic commerce laws; (2) the new personal privacy protection laws; (3) the new electronic discovery guidelines; (4) the new National Standards of Canada concerning electronic RIM ; and (5) the records requirements of government agencies such as the Canada Revenue Agency. This article is therefore a first step in justifying the emergence of the ‘‘RIM lawyer’’ as a new field of legal practice.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".