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Record W4290374764 · doi:10.1353/arn.2022.0015

The Rage and the Kill-Ease

2022· article· en· W4290374764 on OpenAlexaboutno aff
Kevin Solez

Bibliographic record

VenueArion · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRage (emotion)PoetryCharacter (mathematics)ClassicsArtHistoryPerformance artPandemicArt historyCoronavirus disease 2019 (COVID-19)LiteraturePsychologyMedicine

Abstract

fetched live from OpenAlex

The Rage and the Kill-Ease Kevin Solez (bio) The Kill-Ease killed the Horse-Tamer.Manslaughtering hands dragged the horsehair helm.The Charioteer spoke, and spoke, and spoke.Lord-of-Men bled spear-wounded.Lord-of-Men could hardly stand. [End Page 5] Kevin Solez Kevin Solez is currently Visiting Assistant Professor of Classics at Memorial University of Newfoundland and Labrador. He is editor of Pandemic Poems (Kendall Hunt 2020), one of the first records of artistic responses to the coronavirus pandemic. In his academic work he interprets ancient Greek literature using cognitive and anthropological approaches, and is working on a book called Feasting in the Iliad: Structure, Character, and Diplomacy, which is under contract with Brill. Copyright © 2022 Trustees of Boston University

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.998

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 studies0.0030.000
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 head0.014
GPT teacher head0.282
Teacher spread0.268 · 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.

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
Published2022
Admission routes1
Has abstractyes

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