Bibliographic record
Abstract
Introduction We suggested in Chapter 1 that for law to guide human interaction it must be broadly congruent with the practices and patterns in society. Law must rest on a foundation of shared understandings. At the same time, law is also more than simply those understandings; not because it takes a particular form or can be enforced in particular ways, but because it arises only when shared understandings come to be intertwined with distinctive internal qualities of law and practices of legality. In this chapter, we examine these twin propositions in detail. We begin by exploring how shared understandings emerge in international society. To illuminate the underlying processes, we rely on constructivist IR and social learning theories. Next we examine the relationship between shared understandings and international legal norms. We ask what kinds of shared understandings must exist for law-making to be possible, and take a closer look at the transition from social norms to legality. Finally, we address what kinds of shared understandings can exist in a deeply diverse world and one in which power imbalances are so marked. We argue that while our approach may initially seem unduly optimistic in the light of diversity and power imbalances, the interactional approach actually reveals with clarity the limits to international law-making, while also illuminating opportunities. Interactional international law can exist in weak or strong forms; the deeper the shared understandings, the greater the possibility of ambitious law. Limited shared understandings do not mean no law, but they limit the possibilities of law-making.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.012 | 0.029 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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".