Getting at the Root of Evil: Kant and Fichte on the Murderer at the Door
Bibliographic record
Abstract
Abstract In his famous essay, “On a Supposed Right to Lie from Philanthropy,” Kant argues that one is not allowed to lie, not even if a murderer comes to one’s door asking the whereabouts of their innocent victim who has taken refuge in one’s home. Many of Kant’s readers worry that his rigorism concerning the duty of truthfulness leaves us powerless in the face of evil. My aim in this paper is to reconstruct and offer a qualified defense of a Fichtean approach to the duty of truthfulness. I argue that, instead of leaving us powerless in the face of evil, the Fichtean approach gets at the root of evil. This is because Fichte’s prohibition against lying in his System of Ethics goes together with a perfectionist commitment to promote the greatest possible development of rational nature both in ourselves and in all other individuals.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.087 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".