Journalists, Judges, and State Officials: How Russian Courts Adjudicate Defamation Lawsuits Against the Media
Bibliographic record
Abstract
Throughout the 1990s and 2000s, Russian courts heard between 10,000 and 15,000 defamation cases per year, which means that there was one per approximately 14,500 Russians. By comparison, in the UK, often dubbed a ‘defamation tourism’ destination for its supposedly high volume of defamation cases, courts hear only about 250 cases per year, for a per capita rate of one in 1.5 million. In other words, Russia’s per capita defamation lawsuit rate is 100 times greater than the UK’s! Why do Russians generate so many defamation cases? Who sues to protect their dignity and reputation the most often? What are some common triggers of defamation lawsuits? How do Russian judges decide defamation disputes? Who wins and who loses? Who gets a moral damage award and what determines its size? Does the political affiliation of the plaintiff affect their chances in court? The answers to these questions reveal part of the civil justice experience in Russia and illuminate the influence of Russian legal culture over judicial outcomes. They also carry important implications for the state of Russia’s media landscape and the independence of the courts from political interference. To address these questions, this chapter delves into the legislative basis for defamation disputes and the jurisprudence of Russia’s top court on the subject. It also analyses an original dataset that I compiled based on information collected by the Glasnost Defence Foundation (GDF). The dataset contains extensive information on close to 2,000 civil defamation complaints against media outlets, adjudicated by ordinary Russian courts from the majority of Russian federal regions, between 1997 and 2011.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".