Bibliographic record
Abstract
ABSTRACT In our interdependent and complex world, lawyers play an increasingly important role. The legal profession depends on lawyers' commitment to the rules of professional conduct governing how they interact with clients, courts, third parties, and one another. Recent events in both the political and private sphere provide examples where lawyers have fallen short of their fiduciary duties. While scholarship abounds on how lawyers should behave in light of their ethical obligations, our understanding of how lawyers approach these obligations remains underexplored. This article seeks to fill this gap. We conduct an experiment of licensed lawyers in Ontario, Canada where we randomly assign fact scenarios in which respondents stand to benefit or lose from ethically questionable conduct. We find that while a large majority of respondents from each randomized group found the conduct in question violated rules of professional conduct, they were less likely to reach that result if they benefitted from the conduct. And, when asked how most other lawyers would respond, a smaller percentage of both groups thought their peers would find a rule violation. Moreover, this gap between respondents' first‐person and peer perceptions was larger when respondents of both the benefit and harm groups reached a higher consensus that a rule violation occurred. These findings provide evidence that lawyers are confident their own commitment to the rules of professional conduct exceeds that of their fellow lawyers. In the real world where actions matter as much as, if not more than, beliefs, lawyers' differing perceptions between self and others may affect their own fidelity to the rules of professional conduct.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".