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Record W4387247474 · doi:10.1017/s0009640723001385

“We Slay Demons”: Moral Progress and Origen's Pacifism

2023· article· en· W4387247474 on OpenAlexafffund
Jennifer Otto

Bibliographic record

VenueChurch History · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsUniversity of Lethbridge
FundersUniversity of OxfordUniversity of Lethbridge
KeywordsBattlePerfectionPassionsAsceticismPhilosophyOpposition (politics)PersecutionCriticismReligious studiesLawHistoryTheologyPolitical sciencePoliticsAncient history

Abstract

fetched live from OpenAlex

This article evaluates Origen's criticism of Christian participation in the Roman army in relation to two prominent themes in his writings: the moral progress of the Christian and the role of demons in God's providence. I argue that, for Origen, to be a Christian is to be a soldier, albeit one whose adversaries are not human combatants, but the Devil and his angels. The battle is won when Christians refrain from sinning, attaining moral perfection through their study of the scriptures, and adoption of ascetic practices. By avoiding the physical battlefield, Christians remain unsullied by the passions that inflame the soldier, enabling them to fight demons more effectively. But this spiritual combat is not without risks to the physical body. As Origen's Exhortation to Martyrdom attests, execution could be the providentially ordered outcome of a Christian's combat against demons. Origen presents the violent persecution of Christians as consistent with divine providence and martyrdom as a gift of God to the church. His opposition to Christian military participation is rooted neither in a wholesale rejection of warfare nor a deep respect for embodied life, but in his concern for human moral progress—progress that could be advanced by providentially sanctioned violence.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.036
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.316
Teacher spread0.242 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2023
Admission routes2
Has abstractyes

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