Overview: The Rules of Professional Conduct and Their Application to the Legal Profession Online (and Off)
Bibliographic record
Abstract
This article was presented in a modified format at the Law Society of Upper Canada’s Ethical Considerations in an Age of Technology Continuing Professional Development program on October 7, 2011 and November 21, 2011. Reproduced with permission of the publisher from Internet and E-Commerce Law in Canada, Vol. 12, No. 11, March 2012.This article seeks to provide a formal analysis of the ethical obligations applicable to the activities of lawyers carried out online, with particular reference to “social media.” While an exhaustive review of the Rules of Professional Conduct is beyond the scope of this article, an examination of the Rules indicates that the rules listed within are the most relevant to the issue canvassed in this article.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".