Elections, Institutions, and the Regulatory Politics of Platform Governance: The Case of the German NetzDG
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it