Bibliographic record
Abstract
In Germany, far-right groups have revived Nazi terminology like Lügenpresse (lying press) or Systempresse (system press) to decry the media today. German politicians, journalists, and the public have turned to numerous methods to try to combat the reinsertion of Nazi language into everyday German life. One key method is the law. Prior to the 2017 German election, the German parliament swiftly passed the Netzwerkdurchsetzungsgesetz (Network Enforcement Law, NetzDG for short). While English-language press has often called this act a hate speech law, it actually enforces 22 statutes of extant German speech law online. Spearheaded by the SPD-led Justice Ministry, NetzDG represented the most public effort by the German government to push back against the AfD, the far right, and the rise of hate speech in Germany. NetzDG attracted huge global attention as the first major law to fine American-based social media companies for not adhering to national statutes. This article examines why German politicians turned to law as a way to combat the rise of the far right. I explore how NetzDG represented German political understandings of the relationship between freedom of expression and democracy and argue that NetzDG followed a longer historical pattern of German attempts to use media law to raise Germany's profile on the international stage. The article examines the irony that NetzDG was meant to defend democracy in Germany, but may have unintentionally undermined it elsewhere, as authoritarian regimes like Russia seized upon the law to justify their own curtailments of free expression. Finally, I explain the difficulties of measuring whether NetzDG has achieved its goals and showcase a few other approaches to the problems of information in democracies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 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".