A market of black boxes: The political economy of Internet surveillance and censorship in Russia
Bibliographic record
Abstract
In recent years, the Russian Internet has developed according to strong centralizing and State-controlling tendencies, both in terms of legal instruments and technical infrastructure. This strategy implies a strong push to develop Russian-made technical solutions for censorship and traffic interception. Thus, a promising market has opened for Russian vendors of software and hardware solutions for traffic surveillance and filtering. Drawing from a mixed-methods approach and perspectives grounded primarily in Science and Technology Studies (STS), infrastructure studies and the political economy of information networks, this paper aims at exploring the flourishing sector of Russian industry of censorship and surveillance. We focus on two kinds of “black boxes” and examine their influence on the market of Internet Service Providers: surveillance systems known as SORM (System for Operative Investigative Activities), and traffic filtering solutions used to block access to websites that have been blacklisted by Roskomnadzor, the Russian federal watchdog for media and telecommunications. This research sheds light on the vivid debates around controversial technologies which Internet actors must adopt in order to avoid government fines, but which are expensive and complex to implement and raise a number of ethical and political concerns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".