Bibliographic record
Abstract
Until the Supreme Court’s decision in R. v. Neil,1 a lawyer’s duty of loyalty was little discussed by Canadian courts or commentators. Binnie J.’s explicit reliance on the concept of loyalty in Neil, however, brought loyalty out from the shadowy wings of legal discourse to centre stage. It is now common for a lawyer’s fiduciary duties to be considered in terms of loyalty. To paraphrase Lord Templeman in C.B.S. Songs Ltd. v. Amstrad Consumer Electronics Plc.,2 the fashionable claimant in cases involving conflicts of interest asserts a breach of the duty of loyalty. Loyalty seems to be like the new, bright, shiny tool that everyone wants to use, and use all of the time. There is, however, a danger that the useful edge of the loyalty concept may be dulled by use on the stony ground of situations for which it is not well suited. The objective of this article is to examine the origins and scope of the duty of loyalty as it applies to both current and former clients. Particular attention will be paid to the duration of that duty with respect to former clients.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".