Schelling and Levinas on Theodicy and the “Life” of Evil
Bibliographic record
Abstract
To begin, let us recall the two paths that Schelling identifies in 1809 as alone able to give us a non-reductive elucidation of evil: dualism and kabbalism. He writes: To demonstrate that there are but two means for explaining evil—the dualistic, according to which an evil ground-being with modifications supposed as much beneath as beside the good; and the Kabbalistic, according to which evil is explained through emanation or contraction, and that thereby every other system must sublimate evil—[to demonstrate this] would require nothing less than the entire power of a … fundamentally expanded philosophy. The dualist path, setting good and evil either in a vertical (unter) or a lateral relation to each other, and the kabbalistic or ecstatic-instatic path, to which Schelling adds surreptitiously the neo-Platonic terms “emanation” and “distance”—these are the sole approaches liable to do justice to the reality of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".