Bibliographic record
Abstract
The ability to engage in political activity is an essential feature of a democratic society. However, the ability of government lawyers to do so is unclear. While most governments have passed legislation identifying permissible political activity of their employees, it is unclear how the professional obligations of lawyers apply in this context and how these professional obligations interact with this legislation. This article answers these questions. The duty of loyalty to the client requires most government lawyers to refrain from all political activity at the same level of government. The special professional obligations of Crown prosecutors require these lawyers to refrain from all political activity. The duty to encourage respect for the administration of justice requires counsel for courts and tribunals to refrain from political activity to the same extent required of judges and members of these courts and tribunals. Charter considerations will reduce these restrictions only somewhat. However, legislation on the political activity of government employees should be interpreted as a waiver of the duty of loyalty that allows most government lawyers to engage in political activity as permitted by that legislation. The article concludes by making recommendations for legislators and law societies to address this uncertainty.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.047 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".