Transformative suffering, destructive suffering and the question of abandoning theodicy
Bibliographic record
Abstract
This paper defends the striving for a theoretical theodicy against the call of some contemporary theologians to abandon the practice altogether. Essential to the defense is a distinction I propose between the themes of "transformative suffering" and "destructive suffering." I respond especially to the views of Grace Jantzen and Kenneth Surin, suggesting how, in Christian theism, effective themes of theodicy would ground the hope for the healing and redemption of the victims of destructive suffering. In abandoning theodicy in principle, it remains unclear what would support this compassionate hope for the victims. Moreover, by maintaining the category of "destructive suffering," one secures against the danger in theodicy of demeaning or repudiating the traumatic experiences of the victims of radical evil. I go on to explore the implications of these points in seeking for effective themes of theodicy.
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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 | 0.008 | 0.009 |
| 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 | 0.089 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".