Platform governance: the transnational politics of online content regulation
Bibliographic record
Abstract
Billions of people around the world use services like Facebook, Twitter, Instagram, and YouTube every day to access information, engage in conversation, and stay in touch with friends and family. These hugely profitable and popular platforms for user-generated content, operated by large multinational technology companies, have in the past decade created complex systems of private regulatory standards that govern online behaviour and have a significant impact on the social, cultural, and political lives of their customers around the world. Where these systems --- which can be understood by International Relations (IR) scholars as an expression of private authority in global politics --- were once tacitly accepted or ignored by state actors, governments have in recent years increasingly sought to shape the rules and practices deployed by platform companies through various strategies. In some cases, governments have sought to `take back control' and re-assert state authority over this privately-managed domain, while in others they have opted rather to work directly with companies in more collaborative fashion. What explains the variation in how governments intervene in platform governance? The thesis argues that how governments seek to shape, challenge, or contest private platform rule-making can be understood as either fitting in within a collaborative or a contested strategy. Building upon literatures from transnational regulatory politics, the thesis explains variation between these two strategies as the result of an interplay between three factors: domestic demand for change, the ability to supply that change (regulatory capacity and transnational or domestic institutional constraints on that capacity), and normative understandings of an actor's appropriate degree of policy intervention. The plausibility of the argument is demonstrated empirically through three qualitative case studies of key regulatory episodes (the German NetzDG, the Australian AVM Act, and New Zealand’s Christchurch Call) in which different governments have deployed different strategies to affect platform rule-making.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".