Bibliographic record
Abstract
Statement of the problem. Active hostilities began in Ukraine, leading to the imposition of a state of war in the country on 24 February 2022. This has resulted in a mass displacement, with citizens leaving their homes in search of safety and protection. Currently, 3.6 million people have left Ukraine, while 6.5 million people are internally displaced. These unprecedented migration flows have created immense challenges for both the displaced populations and the host communities that strive to accommodate them. In the face of extraordinary challenges, Canada was one of the first countries to come to Ukraine's aid. The Canadian government facilitated the visa process for Ukrainians, allowing them to migrate to Canada. The aim of the work is to analyze and evaluate the impact of migrants on urban development, focusing on the cases of Ukraine and Canada. Methods. In the course of the research and preparation of the article, the author used methods of analysis and synthesis, comparative-geographical and comparative-historical approaches, as well as the method of generalization. Results. This article delves into two critical aspects of Ukrainian migration during the ongoing war: internal displacement within Ukraine and international migration to Canada. We analyze the settlement patterns of migrants and the pressure they exert on cities. The geographical distribution is diverse. IDPs settle both in cities near the frontlines and in western regions in Ukraine. Despite all the risks, Kyiv the capital city of Ukraine, remains highly attractive. When relocating to Canada, migrants are guided by two factors: either having acquaintances, relatives, or friends already living there, or simply choosing a well-known large city. As research indicates, various sectors of urban infrastructure and services face challenges and opportunities under the influence of migration. Canada demonstrates effective integration through its well-developed laws, inclusive policies, and support systems that protect migrants' rights, fight discrimination, and provide essential services. The Ukrainian diaspora plays a significant role in this process by helping newcomers adapt. Ukraine's experience with internal migration caused by armed conflict shows the country's challenges in managing large-scale resettlement. The lack of housing, jobs, and social services pushed local authorities to respond quickly to the situation and learn from the practices of other countries. Our analysis underscores the need for strategic planning and investment to ensure sustainable urban development in the face of large-scale migration. Understanding the implications of these movements is essential for building resilient communities and addressing the complex challenges posed by displacement. The novelty. For the first time, the readiness of Canadian and Ukrainian cities to accommodate migrants was compared. Furthermore, the study analyzed how various sectors of urban infrastructure and services adapt to challenges and take advantage of opportunities created by migration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".