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Record W4210641158 · doi:10.1017/9781139540698.009

Poetry and politics: Caesar to Augustus

2018· book-chapter· en· W4210641158 on OpenAlexaff

Bibliographic record

VenueCambridge University Press eBooks · 2018
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistorical and Literary Analyses
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGrammarLatin AmericansVocabularyReading (process)Computer sciencePoetryLinguisticsMathematics educationPoliticsLiteratureClassicsArtificial intelligenceArtPsychologyPhilosophyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Reading Latin, first published in 1986, is a bestselling Latin course designed to help mature beginners read classical Latin fluently and intelligently. It does this by combining the understanding of continuous texts with rigorous teaching of grammar; it provides exercises designed to develop the skills of accurate translation; and it integrates the learning of classical Latin with an appreciation of the influence of the Latin language upon English and European culture from antiquity to the present. The Independent Study Guide is intended to help students who are learning Latin on their own or with only limited access to a teacher. It contains notes on the texts that appear in the Text and Vocabulary volume, translations of all the texts, and answers to the exercises in the Grammar and Exercises volume. The book will also be useful to students in schools, universities and summer schools who have to learn Latin rapidly.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0150.007

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 head0.031
GPT teacher head0.194
Teacher spread0.163 · 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
Published2018
Admission routes1
Has abstractyes

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