Media and globalization: why the state matters
Bibliographic record
Abstract
Chapter 1 Introduction: Rethinking Media Globalization and State Power Part 2 Part I: States and Internet Regulation Chapter 3 Exporting the First Amendment to Cyberspace: The Internet and State Sovereignty Chapter 4 Where the National Meets the Global: Australia's Internet Censorship Policies Part 5 Part II: States and Communications Reform in Societies in Transition Chapter 6 Negotiated Liberalization: The Politics of Communication Sector Reform in South Africa Chapter 7 State Transformation and India's Telecommunications Reform Chapter 8 The IMF, Globalization, and Changes in the Media-Power Structure in South Korea Part 9 Part III: States, Media, and Regional Cultures Chapter 10 Tensions in the Construction of European Media Policies Chapter 11 The Unsovereign Century: Canada's Media Industries and Cultural Politics Chapter 12 Brazil: The Role of the State in World Television Chapter 13 Epilogue
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".