Are Milgram’s Obedience Studies Internally Valid? Critique and Counter-Critique
Bibliographic record
Abstract
This article challenges the most significant methodological criticism directed at Milgram’s obedience studies, namely, that they lack internal validity because most obedient subjects probably did not believe that the “learner” was actually receiving dangerous electric shocks (Orne & Holland, 1968). This criticism has been bolstered recently by data that claims to show that this was indeed the case (Perry et al., 2020; Hollander & Turowetz, 2017). We argue instead that while Milgram’s experimental paradigm has minor methodological flaws, the resilient issue of believability is actually a red herring, because Milgram’s procedure ensured subjects remained uncertain about the reality of the shocks they were ostensibly delivering. This uncertainty forced all subjects into resolving the experiment’s inherent moral dilemma. That is, would they prematurely end a potentially real experiment and secure the learner’s safety? Or would they continue to inflict “shocks” they believed were perhaps, probably, or even most certainly fake, thus still running the risk of potentially being wrong? We believe the obedience experiments remain, for the most part, internally valid, and that they continue to be externalisable to other moral dilemmas. They help in understanding the perpetration of the Holocaust, contrary to the opposite claim made by some of Milgram’s critics.
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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 | 0.191 | 0.431 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.007 | 0.085 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.023 | 0.033 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".