The Movement of U.S. Criminal and Administrative Law: Processes of Transplanting and Translating
Bibliographic record
Abstract
This article examines the transplanting and translating of law in the domains of criminal procedure and administrative law. The transnational movement of law is full of unexpected twists and turns that belie the notion of the United States as a legal behemoth. Furthermore, the movement of legal procedures which occurs both within and across countries with common and civil law legal traditions challenges preconceived notions of an orderly divide between legal families. While the spread of elements of the U.S. jury system and methods of plea bargaining reveals the powerful influence of U.S. legal ideas, the ways that these procedures undergo processes of translation also illustrates the growing prevalence of legal syncretism. The uneven record of transplanting and translating principles of U.S. domestic administrative law into international organizations exemplifies pluralism in the absence of a global legal hegemon. Our aim is to draw attention to the interaction between international and domestic legal factors and to show that multiple legal products shape processes of transplanting and translating, thus creating a polymorphic legal world often characterized by syncretic practices. The new normal is broad consultation of a range of international models and a facility for translating multiple or even conflicting legal practices. This presents opportunities for positive legal change and complicates efforts to locate clear sources of power in the movement of law.\nGlobalization and the Law: The Next Twenty Years. Indiana University Maurer School of Law, Bloomington, Indiana, April 5-6, 2012.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".