Climate Catastrophe and Stanley Milgram’s Electric Shock “Obedience” Experiments: An Uncanny Analogy
Bibliographic record
Abstract
Awareness of impending climate catastrophe has greatly increased over the last 30 years. Increasing awareness, however, has not translated into decreasing but instead increasing greenhouse gas emissions. This paper aims to shed new light on this perplexing and ultimately destructive positive correlation. It does so by applying a new interpretation of Milgram’s Obedience to Authority “electric shock” experiments to the problem of climate catastrophe. This paper reveals that both the Obedience Studies and climate catastrophe share a crucial common denominator: both involve powerful figures utilising manipulative techniques of bureaucratic organisation to push and pull the functionary helpers below them into contributing to preconceived goal achievement. In both cases, for the functionary helpers to achieve the goals of the powerful, all must agree to contribute to the infliction of harm on a powerless group. Nearly all helpers choose to make their harmful contributions because they not only stand to personally benefit, they also suspect that—with so many other links in the chain participating in goal achievement—they can probably do so with total impunity. It is argued that this comparison may help to better understand the complex, self-reinforcing, yet ultimately destructive relationship shared between fossil fuel corporations, the ideological pursuit of economic growth, political impotence, rapacious consumer demand, and impending climate catastrophe.
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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 | 0.004 | 0.014 |
| 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 | 0.009 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".