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Institutional Fear and Self-Censorship as Mechanisms of Social Adaptation in Highly Regulated Political Systems

Authors: Igor Leonov; Yurii Savchuk;

Institutional Fear and Self-Censorship as Mechanisms of Social Adaptation in Highly Regulated Political Systems

Abstract

This study examines institutional fear and self-censorship as structural mechanisms of social adaptation in highly regulated political systems. The research explores the transformation of fear from an individual psychological reaction into a broader institutional mechanism shaping public communication, professional identity, digital behavior, and patterns of social participation. The paper argues that under conditions of administrative uncertainty, selective enforcement, digital observability, and institutional asymmetry, self-censorship gradually becomes normalized as an adaptive form of everyday social behavior rather than an exceptional response to direct repression. Particular attention is devoted to the relationship between institutional vulnerability, digital visibility, and adaptive restrictions of public expression. The study analyzes how highly regulated political environments contribute to the emergence of cautious communicative practices, professional depoliticization, fragmentation of horizontal social ties, and the gradual erosion of public subjectivity. The article further examines the role of digital platforms and persistent potential observability in transforming contemporary public communication into a space of controlled visibility and anticipatory behavioral regulation. The research proposes interpreting self-censorship not exclusively as a political or cultural phenomenon, but as a socially reproduced mechanism of institutional adaptation that shapes sustainable patterns of limited publicity, managed professional identity, and social atomization.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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