Bibliographic record
Abstract
Scott Hershovitz argues that law is a moral practice. In this response, I argue that he is right that we do well to turn our attention to moral questions. However, I argue that Hershovitz should embrace a more thoroughgoing eliminativism, according to which we don't say that law is a moral practice, but rather say nothing at all about law and address the moral questions directly. Hershovitz says that the rule of law requires us to see legal practices as sources of morality. But that requires settling what a ‘legal practice’ is, reproducing questions that more comprehensive eliminativism enables us to avoid. I argue that the rule of law cannot require seeing legal practices as sources of constraint in advance. Instead, we must always determine what is morally required in light of our practices in an all-things-considered assessment. Hershovitz further argues that lawyers are moral experts; I respond that none of us can claim moral expertise, but all of us have the moral responsibility to make our best assessment of what is morally required of us in a given situation, and we cannot rely on ideas of the law or the rule of law to settle that difficult question.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".