Theorizing Justice under Conditions of Global Legal Pluralism
Bibliographic record
Abstract
Abstract There are distinct challenges to the construction of a theory of justice, in both the national and transnational sphere, under conditions of global legal pluralism. Pluralism shapes theories of global and domestic justice not so much by proposing new normative principles but by challenging prevailing methodological assumptions. Taking John Rawls’s theory of justice as a case study, this chapter illustrates how global legal pluralism complicates Rawls’s idealization of a well-ordered society as requiring the full and effective compliance of citizens with a shared and public conception of justice. Ultimately, the most important way in which global legal pluralism contributes to normative moral theory is by calling into question the ideal that the subject of justice can ever be fixed, that a political society can ever be bounded, or that there is a set of principles or a court of appeal that can order, with some degree of finality, the conflicting jurisdictional claims that bear on moral persons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".