Bibliographic record
Abstract
The purpose of this essay is to offer a reconstruction of Lon Fuller’s critique of Hart’s legal positivism. I show that contrary to the claims of Fuller’s many critics, one can derive from his work a clear and powerful argument against legal positivism, at least in the guise found in the work of H.L.A. Hart. The essence of the argument is that Fuller’s principles of legality posit that the same considerations that count for law’s excellence are relevant also for the determining what counts as law. I contrast this view with Hart’s legal positivism, which acknowledged that the principles of legality are relevant for law’s excellence, but considered them irrelevant for determining the question what counts as law. I argue that the positivist position is arbitrary, and - a point on which I focus - completely undefended. I draw from this point a more general challenge to Hart’s theory of law (as well as that of many of his followers), namely that though claimed to be a true theory of law, it has no resources to explain why this is so. I argue that Fuller’s theory does not suffer from this problem, because Fuller rejected a staple of contemporary jurisprudence - the separation of conceptual and normative jurisprudence.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".