Bibliographic record
Abstract
This project takes the view that, despite being one of the most important subjects in the law,legal ethics is largely unresponsive to the myriad legal-ethical issues that lawyers face. The first broad reason for legal ethics poor responsiveness is what I term legal ethics’ two “information problems”: (1) the absence of meaningful legal precedent, and (2) the fact that ambiguities in relational harm make it exceedingly difficult to detect legal ethics problems. I argue that because of these information problems, legal ethics does not have sufficient source material to contend with many legal-ethical issues. The second broad reason for legal ethics poor responsiveness is a “scope problem”: legal-ethical regimes simply do not contemplate a substantial proportion of the ethical issues that come up in legal practice. This project explores the theoretical implications of these shortcomings and suggests some interventions.
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 | 0.079 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.064 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.016 | 0.032 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".