“Voices from the Island”: Informational annexation of Crimea and transformations of journalistic practices
Bibliographic record
Abstract
After the annexation of Crimea by Russia in March 2014, the peninsula experienced a progressive transition of telecommunication and broadcasting infrastructure under Russian influence, followed by a wave of repression of Ukrainian media. Between 2014 and 2015, dozens of Ukrainian media organizations and independent journalists left the peninsula to continue working in exile. This paper explores the phenomenon of informational annexation using a mixed methods approach consisting of in-depth interviews with media and IT professionals as well as digital ethnography and network measurements. It argues that, besides pressure from pro-Russian authorities, journalistic work in the area is challenged by legal and infrastructural factors such as the absence of legal and financial protections for Ukrainian journalists traveling to Crimea, lack of holistic digital security within media organizations, and increased Internet censorship in Crimea. By analyzing the risk perceptions and digital security practices of exiled and Crimean civic journalists, this paper explores how informational annexation challenges journalistic work on the infrastructural and organizational level, enabling the rise of civic journalism, and how it affects journalists' individual digital security practices. In the context of the current Russian invasion of Ukraine, this research provides insights into some of the informational annexation tactics used by Russians in the occupied Ukrainian territories.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| 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".