Restitution and Other Remedies for Refugees and Internally Displaced Persons
Bibliographic record
Abstract
Abstract This chapter explores restitution and other remedies for refugees and internally displaced persons (IDPs). Most refugees and IDPs never receive any formal redress for the wrongs they have suffered. Yet over the past 30 years, significant progress has been made in advancing international norms on remedies for refugees and IDPs, and experiences in countries from Bosnia and Kosovo to Rwanda and Iraq have strengthened understanding of the challenges involved in translating these principles into practice. Efforts have focused predominantly on the restitution of housing, land, and property (HLP), with the assumption that this is the most pertinent remedy for forced migrants, particularly because it may help enable return as the ‘preferred’ solution to displacement. The chapter assesses these developments and the state of research on this pivotal challenge. It reviews the approaches taken in major peace treaties, court decisions, and standards. The chapter then reflects on five intertwined challenges: (i) developing appropriate data collection techniques and evidentiary standards; (ii) balancing the rights of ‘secondary occupants’ and people in protracted displacement; (iii) mitigating risks associated with HLP restitution; (iv) developing a better understanding of how gender, race, class, and other intersecting power relations influence redress; and (v) moving beyond a narrow focus on property restitution to consider the wider range of losses associated with displacement.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".