<b>Інклюзивність при ревіталізації виробничих територій на прикладі мікрорайону «Чайка» у місті Рівному</b>
Bibliographic record
Abstract
This article presents the importance of integrating the principles of inclusion at all stages of planning and implementing projects for revitalization of neglected, in particular, post-industrial urban production areas. The current Ukrainian regulatory framework in the field of inclusion is analyzed, in particular the requirements of state building codes, as well as current political initiatives, in particular the National Strategy for Creating a Barrier-Free Space by 2030. The authors analyze the principles of inclusion in the urban context, highlight the Ukrainian regulatory and legal basis for creating a barrier-free environment, as well as the experience of applying the principles of universal design, spatial accessibility, and social justice in revitalization practice. The study provides examples of practical implementation of an inclusive approach in the revitalization of urban areas in Ukraine (Lviv, Kyiv) and abroad (Canada, Great Britain), which allows us to identify universal indicators of inclusiveness, such as: physical accessibility, functional flexibility, participatory nature and preservation of cultural identity. The authors also identify key challenges, including the fragmentation of approaches, the lack of mandatory social impact analysis, and formalized public discussion. The article formulates a number of recommendations for adapting an inclusive approach to Ukrainian realities, in particular through regulatory consolidation of participation procedures, development of institutional support mechanisms, and financial incentives for the implementation of universal design.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".