Dangerous Speech as an Atrocity Early Warning Indicator: Measuring Changing Conflict Dynamics
Bibliographic record
Abstract
Dangerous speech, any expression that can increase the likelihood that someone will commit or condone violence against members of another group, can be a powerful early warning signal of impending mass violence. However, one cannot make a list of words that are dangerous, since the effect of the message depends not only on its content, but also on how it is communicated: by whom, to whom, and under what circumstances. What may be benign in one context may be extremely inflammatory in another. Thus, effective monitoring requires deep knowledge of the local setting and structural factors. The spread of dangerous speech online increases its potential impact, but also allows atrocity prevention practitioners access to the speech and provides a window into rapidly changing situations “on the ground.” This paper will review the literature on the connection between speech and violence and explain how dangerous speech on social media – and the responses to it – can serve as a signal of changing conflict dynamics. It will then make the case that civil society, as well as embassy staff, when trained to identify dangerous speech, can serve as a much-needed bridge, bringing local knowledge to government officials and NGOs who can marshal resources for effective interventions.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".