Bibliographic record
Abstract
Abstract: I investigate epistemic limits intrinsic to the analytic method of philosophizing used by Michael Rea (Analytic Theology) and Eleonore Stump (Wandering in Darkness). Rea claims analytic tools limited strictly to the purposes of theory building make analytic accounts “advantageously unsuited to deal with the reality of evil.” Rea's interpretation is conceptually distortive and problematic. I show how his use of the logical-practical distinction is unauthentic in dealing with the challenge of tragic evil and present a broad sketch of what I mean by “tragic.” I then elucidate how Rea abuses the epistemic limits of his method by misaiming analytic tools inasmuch as philosophical doubt cuts against any knowledge garnered in the epistemic practices of human beings experiencing evil. I lastly show that Rea's epistemic problems amplify in the theodicy defence of Stump, who uses an artificial construction of evil as suffering of “mentally fully functioning adults” shorn of any socio-historical location. While Stump's artifice of evil and epistemic doubt provides her the possibility for a general reason of moral sufficiency, she is unable to integrate the biblical narratives she expects will lend added epistemic assistance to her defence, so that her project ultimately founders by never really touching ground.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.114 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".