Tending the Bar: The "Good Character" Requirementfor Law Society Admission
Bibliographic record
Abstract
Every Canadian law society requires that applicants for bar admission be of “good character. ”The author assesses the administration of this requirement and its stated purposes of ensuring ethical conduct by lawyers, protecting the public and maintaining the profession’s reputation. In particular, the premise underlying the use of the good character requirement to fulfill those purposes - that character is the “well-spring of professional conduct in lawyers” - is subjected to critical examination through the theoretical principles of Aristotelian virtue ethics and the empirical evidence of social psychology. The primary thesis of this paper is that as currently justified, administered and applied the good character requirement cannot be defended and must not be maintained. The secondary thesis of the paper, however, is that given the relationship - albeit a qualified one - between character and ethical conduct, a reformed version of the good character requirement can, and arguably should, be maintained by the provincial law societies.
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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.049 | 0.004 |
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".