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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 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)0.002
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 score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.027
Scholarly communication0.0060.004
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.014
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 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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