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Record W3099544645 · doi:10.29173/spectrum85

Beth Caldwell's Reading Experience

2020· article· en· W3099544645 on OpenAlexaffvenue
Shelby Elizabeth Haber

Bibliographic record

VenueSpectrum · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicAutobiographical and Biographical Writing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReading (process)MetaphorArgument (complex analysis)Representation (politics)PhraseSociologyFemininityLiteratureArtGender studiesPhilosophyPoliticsLawTheologyLinguisticsPolitical science

Abstract

fetched live from OpenAlex

Near the end of the nineteenth century, Sarah Grand coined the phrase "New Woman," which was influential throughout the first wave of the feminist movement. This paper examines how Sarah Grand's representation of Beth Caldwell's reading habits in her novel The Beth Book acts as a metaphor for the subversive femininity of the New Woman. My project explores the ways in which Grand's feminist ideals are reflected in The Beth Book through the scenes when Beth is reading. I suggest that Beth's atypical engagement with books as textual and physical objects can be equated to social dissent. However, Grand also portrays Beth reading within educational and marital institutions. These experiences lead Beth's engagement with the text to become similar to common nineteenth-century reading practices. I conclude with the argument that Grand represents any personal engagement with a book, even if it is not especially radical, as capable of re-evaluating systemically-enforced interpretations.

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)0.004
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: none
Teacher disagreement score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.009
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0120.001

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 head0.036
GPT teacher head0.217
Teacher spread0.181 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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