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Record W4206819992 · doi:10.1017/9781108637558.004

The Cosmic Ecology of Violence

2020· book-chapter· en· W4206819992 on OpenAlexaff
Matthew Lynch

Bibliographic record

VenueCambridge University Press eBooks · 2020
Typebook-chapter
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsRegent College
Fundersnot available
KeywordsFraming (construction)Impulse (physics)Power (physics)HebrewHistoryEcologySociologyArtLiteratureArchaeologyBiologyPhysics

Abstract

fetched live from OpenAlex

Chapter 2 examines 6:11–13 where creation had become saturated with violence. The Hebrew חמס often connotes systemic violence, and is thus an appropriate term for capturing the way that violence was understood to fill an environment. Once filled, the land could no longer sustain human, plant, or animal populations. Violence works like termites destroying the wood framing on the house. The house may appear reasonably solid from the outside until the moment before it collapses. There are several auditory terms writers use to describe creation in this crisis state. Some describe it in terms of acoustic tumult or uproar. The sound of violence also reverberates horizontally throughout creation in an ever increasing ‘tumult’, bringing creation to its breaking point. But the tumultuous sound of the violent also sends out shockwaves that reach God, because the basic impulse of violence is heavenward arrogance. This undermines the power of the arrogant because God responds decisively against arrogant claimants to Yhwh’s throne.

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)0.001
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.031
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.028
GPT teacher head0.189
Teacher spread0.161 · 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
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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