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Reading Chaucer in Time

2020· book· en· W4238426465 on OpenAlexaff
Kara Gaston

Bibliographic record

Venuenot available
Typebook
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReading (process)PoetryLiteratureArgument (complex analysis)ArtHistoryPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Abstract Reading for form can mean reading for formation. Understanding processes through which a text was created can help us in characterizing its form. But what is involved in bringing a diachronic process to bear upon a synchronic work? When does literary formation begin and end? When does form happen? These questions emerge with urgency in the interactions between English poet Geoffrey Chaucer and Italian Trecento authors Dante Alighieri, Giovanni Boccaccio, and Francis Petrarch. In fourteenth-century Italy, new ways were emerging of configuring the relation between author and reader. Previously, medieval reading was often oriented around the significance of the text to the individual reader. In Italy, however, reading was beginning to be understood as a way of getting back to a work’s initial formation. This book tracks how concepts of reading developed within Italian texts, including Dante’s Vita nova, Boccaccio’s Filostrato and Teseida, and Petrarch’s Seniles, impress themselves upon Chaucer’s Troilus and Criseyde and Canterbury Tales. It argues that Chaucer’s poetry reveals the implications of reading for formation: above all, that it both depends upon and effaces the historical perspective and temporal experience of the individual reader. Problems raised within Chaucer’s poetry thus inform this book’s broader methodological argument: that there is no one moment at which the formation of Chaucer’s poetry ends; rather its form emerges in and through the process of reading within time.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.373
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0540.006

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 head0.021
GPT teacher head0.192
Teacher spread0.171 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2020
Admission routes1
Has abstractyes

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