The Questionable Presupposition Underlying Hartian Accounts of Legal Facts
Bibliographic record
Abstract
Abstract Per the standard reading of his view, Hart held that the legally valid norms of any legal system are those identified as such by the criteria of validity effectively accepted in common by the system's officials (i.e. the system's rule of recognition). Here, I focus on the presupposition underlying this Hartian account of legal facts – namely, that the officials of any legal system share a perspective that fixes the identity of their system's legally valid norms. Below, I hope to establish the appeal of this presupposition and the attendant Hartian project of providing an account of the identity of the legally valid norms of any legal system. Nonetheless, as I explain, the phenomenon of disagreement that Ronald Dworkin describes threatens Hart's crucial presupposition, thereby posing a mortal threat to his account of legal facts. Moreover, as I hope the following survey illustrates, the phenomenon of disagreement similarly threatens many other contemporary theories of law (including Dworkin's), for these theories join the project of providing a Hartian account of legal facts. A final objective of this survey is to highlight two mutually exclusive strategies for responding to the phenomenon of disagreement: rejecting the Hartian presupposition and commitment to legal facts or affirming this Hartian project by explaining how officials share a legal fact‐fixing perspective despite disagreeing about their system's criteria of validity.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".