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Record W3044861722 · doi:10.3138/tjt-2020-0102

Suffering, Guilt—and Divine Injustice? The Nature and Forms of Evil in Their Bearing on the Problem of Theodicy

2020· article· en· W3044861722 on OpenAlexvenueno aff
Heiko Schulz

Bibliographic record

VenueToronto Journal of Theology · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicTheology and Philosophy of Evil
Canadian institutionsnot available
Fundersnot available
KeywordsTheodicyPremisePhilosophyEpistemologyHumilityProblem of evilInjusticeRelation (database)AgnosticismIncarnationCorollaryTheologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

The so-called problem of evil rests upon the apparent incompatibility of three fundamental hypotheses: God exists; God is inclusively omniscient, omnipotent, and perfectly good; and evil exists. Most apologetic responses to the problem (resulting either in a full-fledged theodicy or a mere defence of God) focus upon the second premise in relation to the third, and the same essentially goes for attempts at proving the insolubility of the problem or at least the actual failure of theodicies and/or defences so far. By contrast, the present article concentrates on the third premise alone and thus aims, first and foremost, at a comprehensive account of (a) the nature and (b) the fundamental types of evil—here with a special emphasis on what ( pace Leibniz) is tentatively dubbed “eschatological evil.” In conclusion, the article expands on the previous analysis by (c) highlighting some of its major—and in fact devastating—implications regarding the possibility of a philosophical theodicy and/or defence. As a corollary, a brief case will be made for a theological—or, more precisely, Christian—view of the problem. I will argue, in particular, that Christians have good reasons to adopt a genuinely agnostic stance toward all purported solutions to the problem of evil, a stance culminating in what has aptly been called “epistemic humility.”

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.234
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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