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Record W3185611227

Teaching Women Writers in a "Great Books" Program

2021· article· en· W3185611227 on OpenAlexaboutno aff
Micheline White

Bibliographic record

VenueHuman Biology · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicPublishing and Scholarly Communication
Canadian institutionsnot available
Fundersnot available
KeywordsArtLiteratureHistory
DOInot available

Abstract

fetched live from OpenAlex

This piece discusses the integration of women writers into a "Great Books" curriculum at a public university in Canada. I consider how the relations among my students, me, and the texts of early modern women have changed over the past twenty years as those texts have become part of the canon. I argue that the availability of high-quality teaching editions and my university's Learning Management System (LMS) have transformed both my students' encounters with female authored texts as well as the kinds of academic labor that I need to undertake to facilitate undergraduate learning. Where I once spent a good deal of time establishing frameworks within which women's writing had value, I now provide much more specialized guidance often helping students grapple with the material idiosyncrasies of early modern texts. Today, my students view women's writing as integral to the curriculum and are far more eager to embark on in-depth research projects. At the same time, these positive developments have often obscured the ways in which patriarchy shaped women's reading and writing, and I have had to underscore the conditions that restricted women's textual agency. My students and I reflect on the implications of teaching women's texts as "great books" and about the kinds of changes we need to make to make the curriculum more racially diverse.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.801

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)0.0010.000

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 head0.064
GPT teacher head0.307
Teacher spread0.243 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2021
Admission routes1
Has abstractyes

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