Ukraine’s Decentralisation Reforms and the Path to Reconstruction, Recovery and European Integration
Bibliographic record
Abstract
The twin concepts of territorial cohesion and competitiveness have underpinned European integration and are fundamental to the development of robust democracies. They speak to the importance of reducing territorial inequalities and ensuring that all places deliver good livelihoods and well-being. Governments can strengthen subnational capacities to help deliver on these objectives through administrative, fiscal and political decentralisation and regional development. Driven by a strong, community-oriented social foundation, Ukraine has pursued this path. Since 2014, it has embarked on ambitious decentralisation, anti-corruption and regional development reforms, and progress has been made in a number of areas, such as service delivery, municipal finance and decision-making. Russia’s full-scale invasion that began in February 2022 has disrupted the reforms and led to massive destruction, especially in Ukraine’s eastern regions. Here I argue that the continuation of these reforms is critical for democracy, reconstruction, recovery and eventual European integration and that the future of the global order rests not just upon the success of countries but also on their constituent regions and communities. The international community has a central role to play in supporting such a place-based approach to territorial development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".