Bibliographic record
Abstract
Abstract: 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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".