The rise of a media empire in the former Communist space : A case study of central European media enterprises
Bibliographic record
Abstract
During the transition from authoritarian regimes to democracy, media have changed along with the other components of social and economic life. They have evolved from propaganda vehicles to real business entities that are functioning in free markets. However, relations between the media, state, society, and market have led to the development of unique dynamics of media systems in the ex-communist countries. Among all media groups that have formed in the ex-communist space, there is one company that stands out. Central European Media Enterprises (CME) - a Bahamas-based American-owned company was the first to bring Western marketing and management styles, along with American programming. CME knew how to commercially exploit the market's underdeveloped potential and consumers' thirst for information and entertainment. This paper looks at CME mainly from a business perspective. It attempts to document and explain the company's strategies and decisions, and the way in which they were influenced by politics. The theoretical framework of the paper builds on the Splichal's concept of "political capitalism" and on Doyle's and Mosco's theories of media economics. The conclusion is that CME's financial success has been highly influenced - though not totally determined - by factors that have nothing to do with a normal, healthy business environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 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".