The Legal Profession's Rule against Vouching for Clients: Advocacy and the Manner That Is the Man Himself
Bibliographic record
Abstract
Modem American lawyers impose on one another regulatory rules that speak to the old argument but have not resolved it. One of these requires lawyers to advocate the interests of their clients with zeal; another forbids them from arguing that they believe what they say, or in the merit of what they are asking the government to do. The latter of these is a rule against vouching for clients. Rules that require zeal and forbid vouching seek to prevent both advertent deceit and an "unprofessional" limitation of advocacy to causes lawyers believe in. My claim is that these rules are as unsatisfactory as the two evils they attempt to prevent. My proposal is to appropriate, instead of these rules, or as a way to live with rules such as these, the understanding of friendship Aristotle developed in his Ethics and in the Magna Moralia.4 This paper is part of a broader argument for Aristotelian virtue ethics in American legal ethics. It is more specifically, and in reference to the moral judgments advocates make, an argument for consideration of the virtue of friendship (or friendliness), and against dependence on ethical analysis of the statements hypothetical advocates make or might make. I will attempt to describe, first, the situation of a modern American lawyer-advocate; then the history behind this situation, with attention to the fact that professional teaching on vouching has turned on the misuse of character, rather than on concern for truthfulness. Then I will attempt to survey appellate opinions from Canada and the United States, for indications on how we lawyers live with rules on zeal and vouching, and how we use them offensively. And, finally, I will offer the Aristotelian alternative and take a look at how it shows up in the anthropology of American lawyers.
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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.053 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".