Suffering, Guilt—and Divine Injustice? The Nature and Forms of Evil in Their Bearing on the Problem of Theodicy
Bibliographic record
Abstract
The so-called problem of evil rests upon the apparent incompatibility of three fundamental hypotheses: God exists; God is inclusively omniscient, omnipotent, and perfectly good; and evil exists. Most apologetic responses to the problem (resulting either in a full-fledged theodicy or a mere defence of God) focus upon the second premise in relation to the third, and the same essentially goes for attempts at proving the insolubility of the problem or at least the actual failure of theodicies and/or defences so far. By contrast, the present article concentrates on the third premise alone and thus aims, first and foremost, at a comprehensive account of (a) the nature and (b) the fundamental types of evil—here with a special emphasis on what ( pace Leibniz) is tentatively dubbed “eschatological evil.” In conclusion, the article expands on the previous analysis by (c) highlighting some of its major—and in fact devastating—implications regarding the possibility of a philosophical theodicy and/or defence. As a corollary, a brief case will be made for a theological—or, more precisely, Christian—view of the problem. I will argue, in particular, that Christians have good reasons to adopt a genuinely agnostic stance toward all purported solutions to the problem of evil, a stance culminating in what has aptly been called “epistemic humility.”
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".