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Record W4412953850 · doi:10.1177/00207152251358845

Echoes of uncertainty: Reimagining complexity of global risks in the shadow of the Russian–Ukrainian conflict

2025· article· en· W4412953850 on OpenAlexvenueno aff
Mateusz Błaszczyk, Piotr Pieńkowski

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
FundersNarodowa Agencja Wymiany Akademickiej
KeywordsUkrainianShadow (psychology)Political sciencePolitical economyEconomic systemSociologyPsychologyEconomicsPhilosophyLinguisticsPsychoanalysis

Abstract

fetched live from OpenAlex

This study examines the perception of global risks in Ukraine and Poland in the context of the ongoing Russian–Ukrainian conflict. Based on cross-sectional survey data, significant differences were found in how respondents from both countries perceive various threats. Ukrainians expressed higher levels of concern across all risk categories, particularly those directly related to the conflict, such as military operations and energy shortages. The results of the principal component analysis (PCA) revealed three distinct factors in Ukraine, indicating a diversified perception of risks, while in Poland, a single factor was identified, suggesting an integrated understanding of threats. These structures can be interpreted through the lens of polycrisis in Ukraine, where different dimensions of risks are seen as interconnected, and metacrisis in Poland, where various risks are viewed as manifestations of a single, overarching crisis structure. The study underscores how geopolitical contexts can shape societal perceptions of threats. It also suggests that the concepts of polycrisis and metacrisis are not only useful theoretical tools for explaining the complexity of contemporary global challenges but also practical frameworks through which individuals reflexively interpret and engage with global risks.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.827

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.183
GPT teacher head0.485
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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