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Commonplacing, Making Miscellanies, and Interpreting Literature

2022· book-chapter· en· W4311886044 on OpenAlexaff
Victoria Burke

Bibliographic record

VenueOxford University Press eBooks · 2022
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLiterature: history, themes, analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMiscellanyInterpretation (philosophy)PoetryCompilerLiteratureAestheticsArtHistoryLinguisticsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract This chapter discusses women’s participation in commonplace book culture and miscellany making, arguing that women’s manuscript compilations can offer unique insights into how early modern women responded to and created discourse. After defining commonplace books and miscellanies, the chapter describes recent research into female readers of John Donne’s poetry. Compilers show evidence of interpreting poetry as they copied it, whether in their choice and ordering of poems and extracts, or in aesthetic ‘improvements’ to them, which raises the issue of early modern women’s interpretation of the writing they read, and scholars’ interpretation of these choices. Scholars can wish to see creative engagement in the compilers they study, but sometimes neither the reasons for particular decisions, nor the gender of a compiler, can be established. Many women in the period did find that this mode of responding to literature and religious texts made a space where constraint could turn into creation.

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)0.006
metaresearch head score (Gemma)0.012
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 score0.012
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0080.023
Scholarly communication0.0120.006
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.019
GPT teacher head0.181
Teacher spread0.162 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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