Bibliographic record
Abstract
Abstract This paper critiques the use of the term ‘evil’ in philosophical discussions of the problem of evil. We argue that what is commonly identified as ‘evil’ in this debate is better as ‘misfortune.’ The division between moral and natural evil equivocates between agentic and non-agentic ‘evil,’ undermining its coherence as a unifying concept. Evil events are necessarily caused by evildoers, which are non-existent in events of natural evil. By contrast, ‘misfortune’ places the focus on the victim regardless of the source, better capturing what philosophers intend with the prior term ‘evil.’ Our more precise definition of ‘evil’ satisfies Jean Nabert’s notion of evil as the unjustifiable while also being sufficiently distinct from badness. What distinguishes ‘evil’ from mere badness is moral erasure, which is the perception of other human beings as objects unworthy of moral consideration. While a bad person causes misfortunes as a trade-off in pursuit of a perceived good, an evil person is either completely indifferent to their victim’s misfortunes, or malicious by deliberately causing misfortunes for pleasure’s sake. Our distinction between ‘misfortune’ and ‘evil’ clarified as (im)moral, indifferent, or malicious challenges the assumption that evil, as traditionally framed, poses a direct contradiction to God’s existence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".