Banal and Fetishized Evil: Implicating Ordinary Folk in Genocide Education
Bibliographic record
Abstract
Genocide education would benefit from a renewed focus on how ordinary people perpetuate atrocities more so than villains. Ordinary evil is often understood via Hannah Arendt’s political theory, which explains how folks can contribute thoughtlessly to genocide. This banality of evil explains an important aspect of human behavior, especially when understood in conjunction with Elizabeth Minnich’s work on intensive and extensive evil, as well as Stanley Milgram’s research on obedience. Yet, Arendt, Minnich, and Milgram do not explain ordinary people who become eager killers. Thus, the addition of Ernest Becker’s idea of the fetishization of evil is important. Students would benefit from engaging with Arendt and Becker’s theories in tandem, as well as from learning about disobedience and ways to expand fetishized perceptions of others.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".