Methodology and Perspective in the Theory of Lawyers' Ethics: A Response to Professors Woolley and Markovits
Bibliographic record
Abstract
Professor Woolley's principal article identifies a fault line in the theory of legal ethics, between those who ask what a lawyer should do in a situation, and those, like Professor Markovits, who are concerned with how a lawyer should be. The firstpersonal turn in legal ethics emphasizes the lawyer's integrity or character, rather than impartial considerations such as the client's interests or legal rights. Professor Markovits, for example, foregrounds the affective process of engagement by clients in adjudication, and from that derives a conception of legal ethics that emphasizes the lawyer's passivity, as a negatively capable conduit facilitating client engagement. Professor Woolley acknowledges that there is a first-personal problem in legal ethics, but insists on separating it from the questions pertaining to the best way to regulate the legal profession. This comment accepts Professor Woolley's distinction between theoretical questions pertaining to regulation and those pertaining to what constitutes a life well lived. It goes beyond her article, however, in denying that considerations of integrity, personal identity, and a life well lived do not bear on impartial questions such as what duties lawyers have to their clients and others.
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 | 0.096 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.092 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.025 | 0.048 |
| 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".