Bibliographic record
Abstract
Abstract In contemporary analytic philosophy, the problem of evil refers to a family of arguments that attempt to show, by appeal to evil, that God does not (or probably does not) exist. Some very important arguments in this family focus on gratuitous evil. Most participants in the relevant discussions, including theists and atheists, agree that God is able to prevent all gratuitous evil, and that God would do so. On this view, of course, the occurrence of even a single instance of gratuitous evil falsifies theism. The most common response to such arguments attempts to cast doubt on the claim that gratuitous evil really occurs. The focus of these two survey papers will be a different response – one that has received less attention in the literature. This response attempts to show that God and gratuitous evil are compatible. If it succeeds, then the occurrence of gratuitous evil does not, after all, count against theism. In the prequel to this paper, I surveyed the literature surrounding the attempts by Michael Peterson and John Hick to execute this strategy. Here, I survey the attempts due to William Hasker, Peter van Inwagen, and Michael Almeida, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".