Religious Disagreement, Religious Experience, and the Evil God Hypothesis
Bibliographic record
Abstract
Conciliationism is the view that says when an agent who believes P becomes aware of an epistemic peer who believes not-P, that she encounters a (partial) defeater for her belief that P. Strong versions of conciliationism pose a sceptical threat to many, if not most, religious beliefs since religion is rife with peer disagreement. Elsewhere (Removed) I argue that one way for a religious believer to avoid sceptical challenges posed by strong conciliationism is by appealing to the evidential import of religious experience. Not only can religious experience be used to establish a relevant evidential asymmetry between disagreeing parties, but reliable reports of such experiences also start to put pressure on the religious sceptic to conciliate toward her religious opponent. Recently, however, Asha Lancaster-Thomas poses a highly innovative challenge to the evidential import of religious experience. Namely, she argues that an evil God is just as likely to explain negative religious experiences as a good God is able to explain positive religious experiences. In light of this, religious believers need to explain why a good God exists instead of an evil God. I respond to Lancaster-Thomas by suggesting that, at least within the context of religious experience, (i) that the evil God hypothesis is only a challenge to certain versions of theism; and (ii) that the existence of an evil God and good God are compossible.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".