'Toxification' as a More Precise Early Warning Sign for Genocide Than Dehumanization? An Emerging Research Agenda
Bibliographic record
Abstract
In genocide scholarship, dehumanization is often considered to be an alarming early warning sign for mass systematic killing. Yet, within broader research, dehumanization is found to exist in a variety of instances that do not lead to aggression or violence. This disparity suggests that while dehumanization is an important part of the genocidal process, it is too imprecise as a salient early warning sign. Genocide scholars have acknowledged such a conjecture in the past. This article initiates an embryonic research agenda that offers ‘toxification’ as a more precise early warning sign for genocide than dehumanization. It contends that while dehumanization signals that killing members of a particular group may be regarded as permissible, a more indicative early warning is one that flags when extermination is considered a necessity. Following a literature review of dehumanization, the purpose of this article is to introduce the idea of ‘toxificaton’, and to illustrate how the concept can work in practice, using two twentieth century genocides as examples.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.059 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".