Bibliographic record
Abstract
Abstract While the term “legal pluralism’ literally denotes a plurality of legal orders, it is their plurality of and the distinguishing features between them, which continues to make the subject matter a very charged and hotly debated one. Seen through the lens of legal sociology and anthropology, the plurality of coexisting, normative orders appears, above all, as a matter of description, as a fact of social ordering. Meanwhile, as some of these normative systems are being claimed as being “law,” while others are associated with nonlegal forms of social order, such as customary, traditional, or indigenous norms as well as, perhaps, sector-specific rules of professional or industry conduct, the categories used to draw the lines between legal and nonlegal norms become in themselves highly contentious. The chapter argues that to neglect the fundamental distinction between legal pluralism as “manifestation” and as “argument” perpetuates a troubling inability on the part of positivist and analytical legal theory to engage with law’s inherent instability. Especially at a time, where the actors, norms, and processes that together constitute and shape emerging transnational regulatory regimes are located and operating both within and beyond the state as the purportedly singularly competent authority of law creation and enforcement, the deconstruction of “legal pluralism” as “nonlaw” and threat to the state can serve as the foundation for a new, critical legal theory.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".