Urbicide in Ukraine: Analysis of Environmental Destruction – Challenges, Strategies, and International Cooperation (Part 1)
Bibliographic record
Abstract
Abstract This article presents the findings of an international research collaboration involving Lviv Polytechnic University (Ukraine), Kielce University of Technology (Poland) and Toronto Metropolitan University (Canada). The research analyses the destruction of the housing environment and the multidimensional consequences of the war in Ukraine. The analyses integrated disparate scientific discussions into a unified research subject and employed novel empirical data from first-hand accounts of witnesses and observers of the war in Kyiv, Kherson, Irpin and Lviv, thereby shedding new light on previously unpublished aspects and consequences of the conflict. The objective of the research was to monitor the situation in areas of active escalation on an ongoing basis, to identify indicators related to urban and demographic dimensions, and to analyse the process of complete destruction of urban organisms together with the communities living in them. The results demonstrate a clear pattern of strategic paralysis in urban structures, the collapse of civilised forms of urban life, a humanitarian catastrophe, the exhaustion of demographic potential and growing socio-economic and geopolitical challenges. The article concludes with an outline of the subsequent phase of the research, which aims to integrate contemporary technologies and methodologies and establish a foundation of prospective projects to develop a unified vision of sustainable architecture and new urbanisation as a guarantor of life stability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".