Bibliographic record
Abstract
All law is customary. This article explores how we should conceive of the customary nature of law, proposing a framework for understanding how legal orders are related to their various societies. The article builds upon the pragmatist conception of law developed by Lon Fuller and Gerald Postema, but it goes well beyond their accounts, arguing that their predominantly functionalist approaches are inadequate. Although law does serve to coordinate social interaction, it does so through specific conceptual languages, through particular grammars of customary law. Law can only be understood if one takes those grammars seriously. The article pursues this argument by drawing comparisons between indigenous and non-indigenous legal orders, both to expand the comparative range and to explore what indigenous legal orders can reveal about law generally. It explores the limitations of functionalist accounts (including law and economics) in the law of persons and property, in presumptions about the foundational requirements of legal order, and in the presence of the sacred or mythic in law. The article concludes that attending to the various grammars of customary law allows one to engage, productively and with insight, in legal reasoning across the normative divide separating different legal cultures.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".