The Government Lawyer as Activist: A Legal Ethics Analysis
Bibliographic record
Abstract
Can a lawyer and government employee represent the government in her professional life while being an activist in her personal life? There is a striking and seemingly irreducible clash, at least at the intuitive level, between the two roles – between representing the government on the one hand while at the same time lobbying it or litigating against it on the other. Government lawyers are nonetheless some of the more successful activists in recent Canadian history. This article analyzes whether this duality is problematic from a legal ethics perspective. The analysis is grounded in three case studies: disability rights activist David Lepofsky, LGBTQ activist Michael Leshner, and rule-of-law activist and whistleblower Edgar Schmidt. It argues that, while activism may engage the lawyer’s duty of loyalty, recusal and—less often—waiver may be sufficient to resolve these loyalty issues. While the simpler and superficially more principled answer is for the prospective activist government lawyer to choose one role or the other, the two roles are not necessarily incompatible—although the activist government lawyer must be particularly alive to the need for recusal, and there will be a point at which the frequency of recusal impairs the lawyer’s ability to do her job.
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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.056 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.008 |
| 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".