State Actor Policy and Regulation Across the Platform-SVOD Divide
Bibliographic record
Abstract
Abstract There are rapidly growing concerns worldwide about the impact of content aggregation and distribution through digital platforms on traditional media industries and society in general. These have given rise to policy and regulation across the social pillar, including issues of privacy, moderation, and cyberbullying; the public interest/infosphere pillar, with issues such as fake news, the democratic deficit, and the crisis in journalism; and the competition pillar, involving issues based on platform dominance in advertising markets. The cultural pillar, involving the impact of SVODs on the ability of content regulation to support local production capacity, is often bracketed out of these debates. We argue this divide is increasingly untenable due to the convergent complexities of contemporary media and communications policy and regulation. We pursue this argument by offering three issues that bring policy and regulation together across the platform-SVOD divide: digital and global players have been beyond the reach of established broadcasting regulation; the nature of the Silicon Valley playbook for disrupting media markets; and platforms and SVODs now need not only to be aggregators but also contributors to local cultures. We draw on three examples: the European Union, Canada and Australia.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".