L'idée de droit naturel à la lumière de la sociologie juridique de Max Weber
Bibliographic record
Abstract
Max Weber's Sociologyof Law provides, according to the author, a strong basis for understanding and discussing not only modern natural law but also contemporary human rights law. In the first part of this article, Weber's sociological analysis of natural law is briefly outlined, mainly in relation to the opposition between formal and material rationality of law. In the Weberian perspective, the antinomy between formal and material natural rights plays a key role in understanding the decline of natural law, and partly explains the irresistible rise of a purely positivistic conception of law. The second part of the study shows how the idea of natural law is in itself inconsistent with Max Weber's epistemological positions. For the author, the Sociology of Law remains closely connected to these positions, which form the basis of the Weberian methodology of (value-free) scientific research. In conclusion, the author emphasizes the importance of Weberian epistemology for the understanding of natural law and, to a certain extent, of contemporary human rights law. He raises doubts, however, as to whether legal positivism can provide a proper comprehension of recent trends in contemporary human rights law. He then suggests that the positivistic conclusion of the Sociology of Law should be reconsidered in light of the reemergence of value-rationality as a basic principle of democratic legal order.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".