An Ethic of Refusal: Simone Weil and the Choice of the Lesser ‘Lesser Evil’
Bibliographic record
Abstract
Abstract In “On the Abolition of All Political Parties,” Simone Weil poses the hypothetical predicament of a person who is intent on solving highly complex mathematical problems but is flogged every time the answer he arrives at is an even number. The person will oscillate between his genuine desire for the truth and the painful cries of his body. “[I]nevitably,” Weil writes, “he will make many mistakes—even if he happens to be very intelligent, very brave and deeply attached to the truth.” She then asks: “What should he do?” Weil’s answer may surprise many readers, even though she claims it is “simple.” If possible, “he must run away” from those who wield the whip. It would have been best, she avers, had he avoided these associations in the first place. Elsewhere in her writings, Weil openly endorses the argument for the lesser evil, justifying active, potentially violent, resistance instead of a pacifist ethic of refusal. This essay analyses the tension between Weil’s ethic of “running away” and her acceptance of the lesser evil.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".