Driving Transformation in War-Related Housing, Land and Property Restitution: Innovations from the Ukraine - Russian War
Bibliographic record
Abstract
Large-scale housing, land and property (HLP) restitution efforts for war-displaced populations face ongoing limitations in three key areas: legal frameworks, financing, and technology. These limitations have led to critical problems in managing population return, reconstruction and political and economic recovery; with particular concern on the emergence of exploitive agendas and social unrest. Ukraine's unfortunate war however presents the opportunity to examine significant advances in mitigating these limitations. Drawing on fieldwork in Ukraine and the author's experience in 23 war-affected countries, the article describes, 1) the socio-legal context and legislative timing of Ukraine's new Compensation Law; 2) the sources, monetization of HLP claims and remedies involved in financing restitution programmes; and 3) the restitution processing speed and social inclusion possible with Ukraine's approach to use of technology.
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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 | 0.002 | 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 | 0.001 |
| 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".