The Politics of Broadcasting in Iran: Continuity and Change, Expansion and Control
Bibliographic record
Abstract
The significant changes that have swept the television industry over the last two decades, most notably a shift to deregulation in broadcast media, prompt a discussion on how to ensure that meaningful content is available to the viewer. Television and Public Policy analyzes the current state of television systems in a selected group of countries by exploring the political, economic, and technological factors that have shaped the sector in such a short span of time. Consequently, by positioning the television sector within issues of media policy and the regulatory framework, the book questions what these trends mean for television, and the historical, political, and cultural role in our societies. \n \nTelevision and Public Policy distinguishes itself in several ways: \n*It is a global project in its comparative scope and subject area. Contributors represent countries including Australia, Brazil, Canada, China, Egypt, India, Iran, Ireland, Israel, Italy, Japan, the Netherlands, New Zealand, Poland, the United Kingdom, and the United States. \n*It is contemporary and filled with information largely absent in current literature. \n*It offers original analysis of the contemporary television sector. \n \nThis book speaks to a broad range of academics, postgraduate, and undergraduate students, and can serve as a key resource for courses ranging from media studies, to development studies, international relations, and law.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".