Structuring land restitution remedies for peace and stability in fragile states.
Bibliographic record
Abstract
Abstract Large-scale dislocation of populations due to land expropriations and armed conflict presents significant difficulties for political stability and food security in fragile states. With increased use of mass claims programs by the international community and governments in order to attend to the problem, attention is focusing on what works. While organizing mass claims programs is challenging, the real difficulty is deriving remedies that are realistic, effective, implementable and that fit the wide variety of circumstances that people, communities and nations find themselves. Although the temptation can be to simply transfer specific remedies from one country to another, in reality these can be difficult to implement with success in places with different cultures, histories, grievances, aspirations and ethnic, sectarian, religious and class divisions. This paper argues that what is more important is the 'structure' of remedy approaches and how these can be adapted to local and national realities. As well, the necessity of any mass claims program to navigate constraints involving inadequate compensation funds, a lack of alternative lands for reparation, a low-capacity administrative environment and a variable willingness to evict current occupants, means that such structures need to be flexible, permutable and adaptable. This review examines the restitution remedy structures that fit these requirements, and that have been successfully implemented in a variety of land and property mass claims programs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".