Streaming platforms: cross-national debates over new regulation (Global watch on culture and digital trade, n°25).
Bibliographic record
Abstract
The June report begins with the discussions toward the Indo-Pacific Economic Framework and its objectives.It also deals with cross-national debates over regulation of audiovisual streaming services in Australia, Switzerland, Denmark and Canada.Then, the report emphasizes the global expansion of several video and music streamers, such as Disney Plus, Amazon Prime Video, Tencent Music Entertainment, Qobuz.In addition, the report turns to public diplomacy actions and strategic partnerships of streaming platforms, focusing on TelevisaUnivision and Netflix. Regulation issues, digital trade and culture Launch of negotiations on the Indo-Pacific Economic FrameworkEnd of May, Joe Biden announced that twelve countries of the Indo-Pacific region, including Australia, Brunei, Indonesia, India, Japan, South Korea, Malaysia, New Zealand, the Philippines, Singapore, Thailand, and Vietnam, have committed to join the US-led Indo- Pacific Economic Framework (IPEF). So far, the IPEF does not incorporate three ASEAN member states (Cambodia, Laos, and Myanmar), Taiwan and China.The IPEF comes five years after the US withdrew the Trans-Pacific Partnership and it is not a free trade deal since "no market access or tariff reductions have been outlined".
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".