Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it