Bibliographic record
Abstract
As governments in Canada and elsewhere have considered statutes to ensure that electronic communications are legally effective, they have invariably had to face questions about the reliability of those communications. Can we trust electronic messages, documents, and signatures? Are they the same in law as if they were on paper? What conditions should be imposed in order to give us the right assurances that we can trust them? To answer these questions properly, we need to understand the nature of “trust” and the extent to which legislation can be a source of it, and what other sources should be enlisted to allow prudent legal operations in the digital age.\nIn this article I raise and suggest answers to a number of questions that arise out of the use of electronic communications. Can we trust electronic messages, documents, signatures? Are they the same in law as if they were on paper? What conditions should be imposed in order to give us the right assurances that we can trust them?\nI will be describing the legal policy responses to the ubiquity of electronic communications, with particular reference to electronic commerce and electronic government, and with a focus on how legislation works in this context.
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 | 0.059 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".