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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 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)0.002
metaresearch head score (Gemma)0.006
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 score0.018
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.019
Scholarly communication0.0080.007
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.021
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; 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

Citations4
Published2020
Admission routes1
Has abstractyes

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Same topicMedieval Literature and HistoryFrench-language works237,207