Tackling Hate Speech in the Digital Space: Germany’s Plans on an Act Against Digital Violence and its Impact on Ethno-Cultural Minorities
Bibliographic record
Abstract
Abstract The increasing significance of online communication services is not yet matched by adequate protections for particularly vulnerable groups, such as minorities, who are disproportionately affected by various forms of digital violence. This article examines a legislative initiative introduced by the German government in 2023, which aims to supplement the EU’s Digital Services Act (DSA) and address certain enforcement gaps in safeguarding individual rights. The analysis reveals that the initiative still faces substantial legal challenges, especially given the harmonizing nature of the DSA, which may require a more refined approach to its ambitious goals. However, a review of European Court of Human Rights (ECtHR) case law concerning the state’s obligation to protect individuals from hate speech suggests that the primary issues outlined in the draft should continue to be pursued. The article contextualizes the initiative within the socio-technical environment where ethno-cultural minorities are targeted online, particularly through hate speech. By situating the initiative within the current socio-legal framework and incorporating a comparative perspective – drawing on regulatory models from Canada, the UK, and Australia – it highlights key challenges that lie ahead.
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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 | 0.008 | 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 | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".