Social Norms and Ethical Entanglements: The Canadian Lawyer’s Risk of Automatic Self-Interest in Ethical Decision Making
Bibliographic record
Abstract
Scholars in the area of behavioural ethics and behavioural economics have established that automatic self-interest can lead to unconscious unethical behaviour. The focus herein is on instances where automatic self-interest stems from ethical ambiguities in Canadian codes of legal ethics, opening the door to possible ethical sanctions where lawyers were not morally blameworthy for the unethical behaviour. This automatic self-interest can be mitigated by the presence of social norms. Herein, a survey of Canadian lawyers is undertaken in order to respond to two central research questions: (1) What are the ethical social norms of Canadian lawyers?; and (2) What methods can be used to alter the current social norms of Canadian lawyers to mitigate automatic self-interest? I respond to these questions while also acknowledging that social norms may differ between sub-groups of Canadian lawyers on grounds such as official language of practice and Indigeneity.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.032 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".