Bibliographic record
Abstract
Abstracts In countries receiving foreign aid, non-state justice systems rooted in custom or religion generally handle most legal disputes. This dramatically influences the prospects of international efforts to promote the rule of law, yet scholars have paid little attention to foreign policy toward non-state justice. This paper explores how the nine largest rule-of-law-assistance providers engaged non-state justice between 2008 and 2018, illuminating the theory behind, and the reality of, donor-state policy. It proposes a new classificatory typology of donor approaches to non-state justice detailing five strategies (denial, acknowledgment, acceptance, transformation, and rejection) and four goals (judicial reform, symbolic recognition, state-building, and counterinsurgency). It then explores how the nine largest rule-of-law-assistance donor states addressed non-state justice through a structured comparison of policy documents as well as case studies of the five donors with the most comprehensive approaches. Donors strongly favored risk-averse approaches, even when this made success unlikely. Certain policy goals—such as state-building or counterinsurgency—sometimes prompted riskier choices, but only with a compelling justification and a reasonable prospect of success. Overall, major rule-of-law donors displayed risk-averse, superficial policy, minimal stakeholder engagement, a failure to grapple with the nuances of legal pluralism, and a lack of evidence to support existing policies.
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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 | 0.000 | 0.000 |
| 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".