Why do pluralistic media systems emerge? Comparing media change in the Czech Republic and in Russia after the collapse of Communism
Bibliographic record
Abstract
A quarter century after the collapse of Communism in the former Eastern bloc, a wide range of scholarly projects have been undertaken to compare and theorize processes of media change in the region. One question that scholars have sought to address is: what were the factors that crucially impacted how these media landscapes evolved? This essay aims to contribute to this debate by juxtaposing media change in two selected cases: the Czech Republic (as a best-case scenario in terms of convergence with the Western model) and Russia (as a scenario where convergence has been limited). Based on secondary analysis of a wide range of sources, the essay systematically exposes 11 crucial differences between the two countries and illustrates how these have impacted the processes of media change. The conclusion sets out how these findings could serve as a starting point and source of inspiration for future comparative research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".