INFORMAL INSTRUMENTS OF FORMAL POWER: CASE OF RUSSIAN MASS MEDIA
Bibliographic record
Abstract
This paper investigates the relationship between the Russian government and mass media businesses. With the state ownership monopoly in the past, transitioning countries do not have evolutionary experience of enforcing corporate law, transparency or protecting minority shareholder rights, and balanced response to stakeholder interests. These represent formal valuable instruments of formal economy. We examine Russia’s recent developments in ownership structure in mass media industries based on insider information – semi-structured interviews with owners and/or top managers of mass media companies from Russian regions, capital cities, and also freelancers who are not affiliated with traditional media companies. With consensus to principles of democratic developments, the share of the state ownership and non-related businesses in Russia’s mass media capital decreased dramatically. Does it mean that mass media companies are becoming independent from the state and oligarchs? We argue that it is still far from being true, and informal pressures and controls over mass media have been developed and are widely used in Russia. We state that loyalty to state/municipal/regional powers (lobbying of their interests) helps these companies to compete against “independent” media. This erosion of principles of independence of mass media in Russia is the result of a corrupted governance model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".