Bibliographic record
Abstract
We have a confession: we have never liked introductory textbooks. Not in history, nor in psychology, and not even in women’s studies. In recent years, we have reassessed our position and we have come to appreciate that a textbook can, in fact, assist instructors and students in navigating the dynamic and swiftly changing terrain of women’s and gender studies. This project grew out of that familiar annual ritual for introductory course instructors: the quest to find the perfect text that will engage and inspire students while guiding them skillfully through the dizzying array of concepts, theories, issues, approaches, histories and contexts that comprise contemporary feminist and gender scholarship. Of course, the perfect text does not, and cannot, exist. Even with a more modest goal in mind, our own attempt at an introductory textbook is proving challenging, and certainly humbling. We revisit the textbook genre and assess its potential value as well as its drawbacks for teaching introductory women’s and gender studies in contemporary Canadian classrooms. We then review ten recent and popular introductory texts on the market in Canada.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.053 | 0.014 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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.
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".