Bibliographic record
Abstract
Abstract As digital platforms have become more integral to not just how we live, but also to how we do politics, the rules governing online expression, behavior, and interaction created by large multinational technology firms—popularly termed ‘content moderation,’ ‘platform governance,’ or ‘trust and safety’—have increasingly become the target of government regulatory efforts. This book provides a conceptual and empirical analysis of the important and emerging tech policy terrain of ‘platform regulation.’ How, why, and where exactly is it happening? Why now? And how do we best understand the vast array of strategies being deployed across jurisdictions to tackle this issue? The book outlines three strategies commonly pursued by government actors seeking to combat issues relating to the proliferation of hate speech, disinformation, child abuse imagery, and other forms of harmful content on user-generated content platforms: convincing, collaborating, and contesting. It then outlines a theoretical model for explaining the adoption of these different strategies in different political contexts and regulatory episodes. This model is explored through detailed case study chapters—driven by a combination of stakeholder interviews and new policymaking documents obtained via freedom of information requests—looking at policy development in Germany, Australia and New Zealand, and the United States.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".