Why to regulate Netflix: the cross-national politics of the audiovisual media governance in the light of streaming platforms
Bibliographic record
Abstract
Today, at a time of major downturn in the audiovisual sector, several regions and countries are reconsidering the scope and reach of domestic or regional audiovisual media governance and are developing policy instruments in order to involve transnational Video on Demand (VOD) platforms, such as Netflix, Prime Video, Disney+, in the financing, distribution and visibility of local, national and regional audiovisual content. A key issue that emerges from this backdrop is to provide convincing answers about why public authorities are feeling the urge to develop new regulations towards global VOD streamers in a specific sequence and temporality and to focus on variables, which are expected to understand this cross-national policy momentum for regulating VOD services. In addition, even though transnational VOD services represent disruptive new actors, creating industrial, technological and institutional shock, this disruption does not lead to the same political issue cross-nationally and to the same kind of policy responses. Firstly, the article explores the key outlines that the academic literature highlights in order to understand the regulation of online platforms in the media sectors. Secondly, it provides a cross-national portrayal of policy initiatives towards the VOD streamers, focusing on the EU Member States, Australia, Canada, Mexico and South Africa. Thirdly, the article argues that political struggles over VOD platforms are expected to be framed and fought simultaneously by two crucial variables, dealing with state-society relationships and global interdependence.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".