Some thoughts on the banality of evil, inspired by a conversation with Jean-Paul Brodeur
Bibliographic record
Abstract
Si la notoriété de Jean-Paul Brodeur est en grande partie due à ses recherches sur la police, il est un autre domaine, plus méconnu du grand public, qui le passionnait et le fascinait, à savoir les formes extrêmes de violence, dont le génocide et le crime contre l’humanité. Dans le cadre de cet article, je prolonge un dialogue que nous avions entamé avec Jean-Paul à propos du concept de banalité du mal, développé par Hannah Arendt. En particulier et à partir des contextes yougoslave et rwandais, je propose une extension de cette notion appliquée non pas tant ici aux fonctionnaires et bureaucrates, tels qu’ils constituaient le centre d’attention de la philosophie, alors concentrée sur l’Holocauste, mais bien plutôt des exécuteurs de terrain. Cette réflexion préliminaire révèle que la notion de banalité du mal peut être déclinée en quatre formes caractérisant la participation des exécuteurs ; soit la banalité de la similitude, de la familiarité, de l’idée et enfin de répétition.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.035 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.015 |
| 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".