Can Ukraine Transform Post-Crisis Property Compensation and Reconstruction? Recommendations for the Diia Platform and eRecovery Program
Bibliographic record
Abstract
The potential for Ukraine's eRecovery program to help transform post-crisis property compensation and restitution globally is hard to overstate. The program allows Ukrainians whose homes have been damaged or destroyed by Russian aggression to apply for and receive compensation through the Ukrainian government's Diia e-government platform. This innovation marks the first-ever example of a compensation process for damaged or destroyed property that is implemented digitally, at scale, while hostilities are ongoing. If managed effectively, it could significantly decrease the time and costs of getting displaced persons back into their homes, while increasing the transparency and security of the property return process. The program faces significant but addressable challenges, including legal ambiguities, technical limitations, and practical issues in implementation. The eRecovery program is a dynamic case study of government innovation that highlights the complexities and opportunities in digital governance for crisis recovery. It also suggests a need for active refinement to ensure effectiveness and inclusivity in addressing widespread displacement and property damage.The efficiency and fairness with which a country can restore property rights to victims of conflict plays a decisive role in that country's post-crisis recovery and trajectory toward stability. The success and potential replication of Ukraine's approach could highlight the transformative power of digital public infrastructure to strengthen crisis management and recovery efforts while limiting the potential for corruption. The current moment presents a significant and time-sensitive opportunity to help shape the eRecovery program to better serve the needs of all Ukrainians. Included in this report are recommendations designed for Ukrainian government administrators and international partners supporting humanitarian and recovery efforts. Recommendations fall broadly into two categories: some are specific to interoperability and the Diia platform, and others encompass recommendations to foster an effective, fair, and trusted recovery program.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".