Disconcerting Insights: Milgram’s Obedience Experiments, Elias’s Civilizing Process, and the Perpetration of the Holocaust
Bibliographic record
Abstract
Social psychologist Milgram (1963, 1974) and sociologist Elias ([1939] 2000) are undisputed social science heavyweights whose scholarly contributions delve into the shared topic of violence. Despite this similarity, near nothing has been written on any insights one might offer the other. With the aim of bucking this trend, this exploratory article illustrates how certain connections shared between both magna operas are mutually beneficial: Elias’s thesis can shed new light into otherwise mysterious obedient subject behavior and Milgram’s experiments can be used to bolster a central yet weak pillar in Elias’s thesis. The strengthening of this weak pillar is of particular importance because it likely reinvigorates the ability of the Civilizing Process to offer unique and counterintuitive insights into German perpetrator behavior during the Holocaust. It is through these Milgram-Elias linkages that the author’s paradoxical concept of civilized killers emerges.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".