From Soviet Shadows to Digital Eyes: Civilian Participation and the Transformation of State Surveillance in Ukraine Through the Lens of Foucauldian Discipline and Control
Bibliographic record
Abstract
Since gaining independence in 1991, Ukraine has undergone a profound transformation in the nature of state surveillance, shaped by its Soviet legacy, digital innovation, and intensifying conflict with Russia. The widespread adoption of smartphones, social media platforms, and open-source intelligence (OSINT) tools has enabled civilians to become direct contributors to surveillance processes, thereby reshaping traditional power relations between the state and its population. This participatory transformation has become particularly pronounced following the annexation of Crimea in 2014 and the full-scale invasion in 2022, both of which significantly intensified Ukraine’s reliance on surveillance technologies under the rationale of national defence. Through smartphone-based reporting, social media activism, and grassroots war crime documentation, civilians now play a pivotal role in intelligence gathering and open-source investigations of alleged human rights violations. This study employs a qualitative methodology--combining historical analysis, case study research, and reviews of legislative and technological developments--to examine the evolution of Ukraine’s surveillance apparatus. The analysis is grounded in Michel Foucault’s theory of disciplinary power, and supplemented by Gilles Deleuze’s concept of “societies of control” and Haggerty and Ericson’s “surveillant assemblage”. These theoretical lenses reveal a hybrid structure where remnants of centralized, top-down monitoring persist alongside emerging decentralized, data-driven modalities influenced by growing civilian engagement.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".