Bibliographic record
Abstract
In this essay I argue that in order to understand debates in jurisprudence one needs to distinguish clearly between four concepts: validity, content, normativity, and legitimacy. I show that this distinction helps us, first, make sense of fundamental debates in jurisprudence between legal positivists and Dworkin: these should not be understood, as they often are, as debates on the conditions of validity, but rather as debates on the right way of understanding the relationship between these four concepts. I then use this distinction between the four concepts to criticize legal positivism. The positivist account begins with an attempt to explain the conditions of validity and to leave the question of assessment of valid legal norms to the second stage of inquiry. Though appealing, I argue that the notion of validity cannot be given sense outside a preliminary consideration of legitimacy. Following that, I show some further advantages that come from giving a more primary place to questions of legitimacy in jurisprudence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.013 | 0.133 |
| Scholarly communication | 0.020 | 0.037 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.014 |
| 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".