Bibliographic record
Abstract
Abstract This article is a response to Stephen Law's article ‘The evil-god challenge’. In his article, Law argues that if belief in evil-god is unreasonable, then belief in good-god is unreasonable; that the antecedent is true; and hence so is the consequent. In this article, I show that Law's affirmation of the antecedent is predicated on the problem of good (i.e. the problem of whether an all-evil, all-powerful, and all-knowing God would allow there to be as much good in the world as there is), and argue that the problem of good fails. Thus, the antecedent is unmotivated, which renders the consequent unmotivated. Law's challenge for good-god theists is to show that good-god theism is not rendered unreasonable by the problem of evil in the same way that evil-god theism is rendered unreasonable by the problem of good. Insofar as the problem of good does not render belief in evil-god unreasonable, Law's challenge has been answered: since it is not unreasonable to believe in evil-god (at least for the reasons that Law gives) it is not unreasonable to believe in good-god. Finally, I show that – my criticism aside – the evil-god challenge turns out to be more complicated and controversial than it initially appears, for it relies on the (previously unacknowledged) contentious assumption that sceptical theism is false.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".