Bibliographic record
Abstract
Abstract Mark McCreary has argued that I cannot consistently advance both the hiddenness argument and certain arguments for religious scepticism found in my book The Wisdom to Doubt (WD). This reaction was expected, and in WD I explained its shortsightedness in that context. First, I noted how in Part III of WD, where theism is addressed, my principal aim is not to prove atheism but to show theists that they are not immune from the scepticism defended in Parts I and II. To the success of this aim, McCreary's arguments are not so much as relevant, for a thoroughgoing scepticism embracing even the hiddenness argument is quite compatible with its success. But I also explained how someone convinced that the hiddenness argument does prove atheism escapes the grip of religious scepticism because of that argument's reliance on apparent conceptual truths. McCreary's critique obscures this point but does not defuse it.
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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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.033 | 0.060 |
| Insufficient payload (model declined to judge) | 0.004 | 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".