Socio-Legal Instabilities in Ukraine's Wartime Compensation Law for Damaged and Destroyed Residential Property
Bibliographic record
Abstract
Abstract Laws seeking to resolve war-related problems face a significant dilemma. While the legal establishment in a war-affected country drafts laws based on normative approaches suited to peacetime and stable settings, the civilian population pursues crises livelihoods that are markedly unsuited to compliance with or use of such laws. What emerges are socio-legal instabilities that aggravate instead of resolve wartime problems. With a socio-legal examination of Ukraine’s wartime housing Compensation Law, this article describes six sets of instabilities that compromise the utility of the law and aggravate or create additional problems: (1) the case-by-case approach, (2) administrative and institutional capacities, (3) legal vs. available evidence, (4) the timeframe for claims submission and awareness raising, (5) excluded segments of civil society and (6) prohibitions on selling properties. Approaches from international best practice that may be able to attend to these instabilities are then suggested.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".