Service to the Nation: A Living Legal Value for\nJustice Lawyers in Canada
Bibliographic record
Abstract
Lawyers working within a living government require a living ethics, an approach to ethics that accounts for their day-to-day professional lives within the Department of Justice Canada. There are different archetypes of Justice lawyers, and thus a living ethics is also an ethics of place, one which is sensitive to the government institutions within and for which lawyers work and the functions they accomplish. The focus of this paper, which employs a virtue ethics methodology, is primarily civil litigators. Distinguishing between values (enduring beliefs that influence action) and ethics (the application of values in practice), the paper proposes "service to the nation" as a value that all Justice lawyers share, and describes how that value grows into an ethics that is specific to Justice civil litigators. Service to the nation comprises in its very fabric a conception ofpublic service which, in turn, requires an investigation of who Justice lawyers' clients are and how the lawyers' public interest mandate informs their professional lives. In the language of virtue ethics, "service to the nation" is the "characteristic function" common to all Justice lawyers. Virtue ethics teaches that the ethical practice ofa lawyer can be facilitated through developing professional mentorship relationships with virtuous people. Both rules and roles provide ethical direction, but it is role models, not rules in professional codes of conduct, that are the focal point of ethical deliberation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.011 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".