Elections, Institutions, and the Regulatory Politics of Platform Governance: The Case of the German NetzDG
Bibliographic record
Abstract
Policy proposals for higher rules and standards governing how major user- generated content platforms like Facebook, Twitter, and YouTube moderate socially problematic content have become increasingly prevalent since the negotiation of the German Network Enforcement Act (NetzDG) in 2017. Although a growing body of scholarship has emerged to assess the normative and legal dimensions of these regulatory developments in Germany and beyond, the legal scholarship on intermediary liability leaves key questions about why and how these policies are developed, shaped, and adopted unanswered. The goal of this article is thus to provide a deep case study into the NetzDG from a regulatory politics perspective, highlighting the importance of political and regulatory factors currently under-explored in the burgeoning interdisciplinary literatures on platform governance and platform regulation. The empirical account presented here, which draws on 30 interviews with stakeholders involved in the debate around the NetzDG’s adoption, as well as hundreds of pages of deliberative documents obtained via freedom of information access requests, outlines how the NetzDG took shape, and how it overcame various significant obstacles (ranging from resistance from other stakeholders and the European Union’s frameworks against regulatory fragmentation) to eventually become law. The article argues, throughout this case study, that both domestic politics and transnational institutional constraints are crucial policy factors that should receive more attention as an important part of platform regulation debates.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".