"Uncivil by too much civility"?: Critiquing Five More Years\nof Civility Regulation in Canada
Bibliographic record
Abstract
The author revisits criticisms of the civility movement made in an earlier paper ("Does Civility Matter?" (2008) 46 Osgoode Hall LJ 175). She argues that Canadian law societies remain concerned with lawyer incivility, despite bringing surprisingly few formal prosecutions against lawyers for incivility. In a few cases the law societies' concern can be justified insofar as lawyer incivility in those cases appears to correlate with serious professional dysfunction. Generally however, the focus on incivility is counter-productive. First, in several cases the focus on lawyer incivility elides the complex and difficult ethical issues raised by the behaviour of the lawyers in question. Disciplining lawyers for incivility when their conduct was substantively unethical avoids consideration of precisely why their conduct was improper, and ignores the implications of that analysis for the ethical duties of lawyers more generally.Second, the civility movement envisages a narrow conception of the "good lawyer" and risks reifying a patrician model of advocacy Finally, civility regulation has the potential to chill proper advocacy particularly for vulnerable clients. Law societies who discipline lawyers for making the right argument in words that were poorly chosen discourage what they ought to encourage.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.068 | 0.030 |
| Scholarly communication | 0.022 | 0.005 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".