Bibliographic record
Abstract
This essay criticizes Jeremy Waldron's assumption that debate about judicial review of statutes requires us to suppose, when it comes to moral reasoning about the law, that either judges or legislatures are good at it and, based on that supposition, to choose which institution should be assigned the task. Waldron does not hold fast to his own assumption—his claim turns out to be that legislatures should have final authority in moral matters, since they are better at moral reasoning than judges. He thus abandons a central tenet of political positivism—namely, its hostility to judges having any role at all in moral reasoning about the law. Thus, Waldron is engaged, not in an argument against judges playing such a role but, rather, in a debate about how to best to design legal institutions, given that judges will, in fact, have an important role in that moral deliberation.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".