Bibliographic record
Abstract
In recent years there has an ongoing debate between two versions of legal positivism. According to one, called exclusive positivism, whenever the law refers to morality, the law necessarily directs its subjects to an external, non-legal, standard, because there is a conceptual impossibility in incorporating moral standards into the law. According to the rival inclusive positivist position, such incorporation is possible, and therefore moral standards can be (although they need not be) part of the law. In this article I argue that both views are mistaken since they both assume that whenever words like `equality`, `justice` etc. appear in the law they refer to moral standards. Rather, I argue, these words refer to legal standards, which are different from the moral standards. As a result the question of the possibility of incorporation can be avoided, and the debate between exclusive and inclusive positivists put to rest.
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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".