Bibliographic record
Abstract
This paper draws together and engages with two recent – and independent – discussions of the problem of evil. Bruce Russell (2018) examines four arguments for atheism that appeal to suffering. He rejects the first three, but defends the fourth. Meanwhile, separately, William Hasker has discussed close variants of the third and fourth arguments. In an important but underappreciated series of papers, he criticizes the former (Hasker 1992, 1997, 2004b, 2008). More recently, he has deployed this criticism against the latter as well (Hasker 2019). The order in which Russell treats these four arguments is helpful and instructive, and so I will follow it. I will briefly discuss the first and second. I will then set out Hasker’s criticism of the third argument, and offer some resistance to his most recent defence of it. I then turn to the final argument, which I call the argument from excessive gratuitous evil. Russell and Hasker both think that it constitutes a formidable problem for theism. I agree. I do not discuss Russell’s (indirect) defence of it. Instead, I examine Hasker’s latest objections to it – including his new deployment of his earlier criticism – and I find them all wanting.
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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 | 0.004 | 0.009 |
| 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 | 0.035 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".