Consequences for Broken Political Promises: Lawyer-Politicians and the Rules of Professional Conduct
Bibliographic record
Abstract
Politicians sometimes break their promises. Canadian law is clear that the only recourse is for voters at the ballot box. However, politicians who happen to be lawyers are ostensibly governed by the rules of professional conduct. Under these rules, certain promises, termed undertakings, are special. Courts and law societies will enforce these promises and/or impose consequences for their breach against practicing lawyers. This article considers how the rule on undertakings should apply to political promises made by lawyer-politicians. The article begins with a brief summary of the rules of professional conduct as they apply to lawyer-politicians and to lawyers’ undertakings. The article then turns to the case law on broken political promises. Finally, the article argues that the rule of professional conduct on undertakings should apply to lawyer-politicians’ promises in limited circumstances, and specifically when these promises take pseudo-legal form such as signed contracts and pledges.
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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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".