Civilian Surveillance in the War in Ukraine: Mobilizing the Agency of the Observers of War
Bibliographic record
Abstract
The war in Ukraine sees local and foreign civilians play active roles in the conflict, mainly through the participatory gathering and sharing of intelligence and open-source investigations of alleged human rights violations and war crimes. These surveillance practices seen in the war in Ukraine are not novel. Vigilantism campaigns have normalized since the War on Terror, while open-source information is increasingly recognized as a legitimate tool for human rights and international criminal justice investigation. Yet, their importance in the war in Ukraine highlights the agentic power of civilian surveillance. The proliferation of digital technologies empowers civilians to become inevitable actors in all spheres of politics, including war. However, across these practices, I argue that the Ukrainian government and its Western allies harness this agency as operational and narrative weapons. Patriotism and morality are pushed forward to mobilize individuals to participate in the war despite the risks that vigilantes and open-source investigators have to assume: risks of retaliation by Russian forces and lost independence.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.008 |
| 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".