Bibliographic record
Abstract
H. L. A. Hart made a famous claim that legal positivism somehow involves a "separation of law and morals." This Article seeks to clarify and assess this claim, contending that Hart's separability thesis should not be confused with the social thesis, the sources thesis, or a methodological thesis about jurisprudence. In contrast, Hart's separability thesis denies the existence of any necessary conceptual connections between law and morality. That thesis, however, is false: There are many necessary connections between law and morality, some of them conceptually significant. Among them is an important negative connection: Law is, of its nature, morally fallible and morally risky. Lon Fuller emphasized what he called the "internal morality of law," the "morality that makes law possible." This Article argues that Hart's most important message is that there is also an immorality that law makes possible. Law's nature is seen not only in its internal virtues, in legality, but also in its internal vices, in legalism.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.074 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".