Bibliographic record
Abstract
Abstract Since the inadequacy of the traditional theoretical frameworks for the study of the “global transformation of modernity” (Beck) was becoming more and more evident in the last decades of the twentieth century, “culture” has figured prominently in many literatures that engage with the post-national condition. Yet in legal academia, despite studying similar phenomena of displacement, fragmentation and hybridization, cultural analysis perspectives have traditionally played a rather marginal role in the discourse on globalization and transnationalization. Although some authors have indeed attempted to operationalize the concept of culture in grappling with effects of legal globalization, the emerging field of “transnational law” never took a significant “cultural turn”. This chapter retraces this disciplinary development and reflects on the use of “culture” in transnational law discourse. While not advocating a more prominent role for the notoriously difficult concept of culture, this brief survey serves as a reminder that the same substantive and theoretical choices that kept transnational law from drawing more heavily on cultural analysis and traditional, “social fact” legal pluralism also may limit its scope and create theoretical blind spots. Not determined by a distinct “body of law” but rather understood as a developing discourse within a discipline in the process of coming into its own, transnational law and its gatekeepers have to decide just how methodologically and substantively inclusive, interdisciplinary, and critical they want it to be.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".