Ukraine's tenurial tangle: Housing, land and property restitution in the Russian war
Bibliographic record
Abstract
Abstract The severity of the population dislocation and destruction of housing, land and property (HLP) in the Ukraine war has driven efforts for starting reconstruction planning prior to the war's end. This comes with the realization that recovery will entail considerable preparation, including efforts at using seized Russian assets to finance it. Engaging in HLP restitution and compensation will be a primary recovery challenge, with the Ukrainian government moving forward with legislation for facilitating this. However, the government's current approach to processing what will be millions of HLP claims for restitution and compensation faces a daunting challenge. Housing, land and property rights prior to the war comprised a dense tangle of confusion, corruption, and inadequate documentation; such that attempting to untangle each claim on a case‐by‐case basis as currently planned is highly problematic and risks instability. This article describes this tangle as five categories of problems: (1) the post‐Soviet transition, (2) rule of law problems, (3) administrative tangles, (4) corruption, and (5) war‐related issues. The article then recommends that the government and international community pursue a ‘mass claims and transitional justice’ approach to large‐scale HLP restitution which is aligned with international best practice and able to supersede the tenurial tangle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".