Counteracting Hate Speech as a Way of Preventing Genocidal Violence
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
Hate speech regularly, if not inevitably, precedes and accompanies ethnic conflicts, and particularly genocidal violence. Without such incitement to hatred and the exacerbation of xenophobic, anti-Semitic, or racist tendencies, no genocide would be possible and persecutory campaigns would rarely meet with a sympathetic response in the general public. In order to successfully prevent genocidal crimes and violence, therefore, it is indispensable to effectively address the problem of systematic incitement to hatred. While less virulent forms of hate speech may be adequately addressed by human-rights law obligations on governments to prohibit such acts, vicious, systematic, and state-organized hate propaganda should be criminalized under international law. Before discussing how hate speech can be treated as an international crime, this article assesses the most important justifications for proscribing hate speech, including the need to protect the human dignity and equality rights of the victims of such speech as well as the need to protect the public peace and the dangers of hate speech in that it may contribute to the creation of a climate of hatred and violence directed against a specific group. The article supports treating systematic incitement to hatred as a form of persecution, an approach recently upheld by the Appeals Chamber of the International Criminal Tribunal for Rwanda. Such an approach most adequately reflects the nature of hate speech and the motivations underlying its criminalization, while also respecting the important right to freedom of speech.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| 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