Legal Ethics and the Attorney General: A Canadian Analysis
Bibliographic record
Abstract
In Canada, the Attorney General holds a complex and unique role within the federal, provincial, and territorial governments. Despite this key position, there is relatively little knowledge and understanding of the role and professional responsibilities of the Attorney General among the public, the media, policymakers, and politicians – including at least some Attorney Generals themselves. Legal Ethics and the Attorney General adopts a doctrinal approach to examine and explain how legal ethics, and particularly the law of lawyering, applies to the Attorney General. The book illustrates that, while the role of the Attorney General is unique, the individual occupying this position practises law and should be held to the same standards as any other lawyer. It addresses common misconceptions: that the Attorney General is not truly a lawyer, that actions deemed wrongful for other lawyers may not be considered wrongful for the Attorney General, or that the accountability measures appropriate for lawyers do not apply to the Attorney General. Ultimately, Legal Ethics and the Attorney General reveals the importance of the accountability of the Attorney General, especially to the provincial and territorial law societies that serve as regulators of the legal profession. This accountability is essential not only for upholding the rule of law but also for enabling these societies to fulfil their statutory mandates to regulate the legal profession in the public interest.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".