The No-Minimum argument, satisficing, and no-best-world: a reply to Jeff Jordan
Bibliographic record
Abstract
Abstract Peter van Inwagen's ‘No-Minimum’ argument boldly rejects a proposition widely accepted by theists and atheists alike: God and gratuitous evil are incompatible. Jeff Jordan (2003) criticizes van Inwagen's argument and (Jordan 2011) defends his position against Michael Schrynemakers (2007). I present two criticisms of Jordan. Concerning his first paper, I argue that if it is plausible to suppose that there exist undetectable evils, Jordan's argument is incomplete. Concerning his second paper, I show how Jordan fails to engage adequately with Schrynemakers's reply and, more seriously, with the notion of satisficing implicit in van Inwagen's No-Minimum argument. To draw out this second criticism, I make use of another debate in the philosophy of religion: the problem of no-best-world.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".