Bibliographic record
Abstract
This Article explores a decision by a German postwar court-the Case of the GrudgeInformer-which was central to the 1958 debate between H.L.A. Hart and Lon L. Fuller.The author argues that Fuller's presentation of the problem in the case is better than Hart's both as a descriptive matter and as a matter of promoting a morally responsible resolution-not least because Hart's method of candor falls short of illuminating the complexities inherent in such cases.In particular, Hart's positivist conception of law does not appreciate how judges in such cases have to contend with a connection between the doctrinal level and the fundamental level.At the former, judges have to resolve issues of substantive law such as the issues of criminal law in the Grudge Informer Case.At the latter, judges confront the question of what Fuller called their "ideal of fidelity to law," since they are faced with questions about what legality-the principles of the rule of law-requires.The confrontation between such ideals is not, as Hart suggested, one that takes place in an extralegal political space.Rather, it is firmly within the scope of both law and the philosophy of law.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.017 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".