Perceived Social Cohesion in Ukraine: Diversity and Attitudes
Bibliographic record
Abstract
This article demonstrates the ways in which social cohesion as a “sense of togetherness” is progressing within Ukrainian society—a society that is striving to escape the post-Soviet model as it undergoes the processes of state- and nation building and democratic development. This study draws on a national population survey and applies cluster analysis to identify homogeneous groups of the population in terms of their social-cohesion perceptions and behaviours. Six clusters are identified: distrustful, disunited, ambivalent, tolerant, connected, and declarative. The authors establish the composition of each cluster in relation to socio-economic, socio-demographic, ethnocultural, and attitudinal characteristics. Their research questions the relevance of institutional trust as a social-cohesion indicator in the context of the specific conditions of transitional societies. The authors submit that trust in political institutions might strengthen social cohesion at the level of society without necessarily corresponding to individually oriented indicators of social cohesion, such as civic and political participations. This paper sheds light on the weaknesses of the methodological approach advanced by Joseph Chan and colleagues. With the application of cluster analysis in the present study, one finds that the horizontal dimension of social cohesion is particularly well suited for use in cross-cultural studies. By contrast, the vertical dimension emerges as more contextual, requiring greater attention to the specificities of a given political regime. This paper proposes the existence of social-cohesion zigzags displaying an ambivalent state of perceptional schemes where the highest cohesion scores in some indicators are accompanied by the lowest ones in others within the same representative group. This study confirms the complexity and multi-level nature of social cohesion in transitional societies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".