Foreword: Transdisciplinary Conflicts of Law
Bibliographic record
Abstract
This introduction to our co-edited special issue of Law and Contemporary Problems addresses how interdisciplinary studies might contribute to the revitalization of the field of Conflict of Laws. The introduction surveys existing approaches to interdisciplinarity in conflict of laws - drawn primarily from economics, political science, anthropology and sociology. It argues that most of these interdisciplinary efforts have remained internal to the law, relating conflicts to other legal spheres and issue areas. It summarizes some of the contributions of these projects but also outlines the ways they fall short of the full promise of interdisciplinary work in Conflicts scholarship, and indeed often replicate the very shortfalls of Conflicts doctrine that they set out to overcome. Drawing on examples from the symposium, the article then argues that there is much to be gained - in both law and other fields - from a more "external" interdisciplinarity that engages nonlegal disciplines such as economics, political science, and anthropology in a more serious and sustained way. It outlines a number of ways cross-disciplinary engagement, like the kind in this symposium, can push the project further: by approaching the study of conflicts through its discourse and imagery, through the historical and present-day context of colonialism, and through ethnographies that detail how its doctrines are experienced and produced in the real world. The final section discusses how the interdisciplinary insights yielded by the symposium might provide a richer and more productive techniques and practices for addressing conflict of laws problems.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".