Women, Education, and Agency, 1600-2000 (2009) Jean Spence, Sarah Jane Aiston, & Maureen M. Meikle, editors
Bibliographic record
Abstract
Women, Education, and Agency, 1600-2000 is an ambitious collection that brings together 13 chapters examining women and education.The book explores women's experiences in education in both formal and informal settings and in various countries and time periods.The book spans 400 years and is ordered chronologically, ending with an in-depth analysis of the history, efficacy, and future of women's centers on university campuses in the United States.The editors write in the preface that the volume has a global perspective and that the book is a celebration of how women have engaged with educational institutions and processes.The chapters are diverse and include topics such as: Claire Jones' chapter of exploring women's involvement in mathematics in 1900 at the University of Cambridge; Barbara Bulckaert's chapter of women who, through free time and access to financial means, were able to tutor themselves when tutors were unavailable; and Barnita Bagchi's chapter of exploring social capital and gender in India through the cases of Ramabai and Rokeya, who were two female activists fighting for female education in India at the turn of the 20th century.Finally, the book draws on many different ways of recovering history and includes case studies of specific women's histories, the involvement of women in different fields, and women's engagement with advocacy.The collection engages with the perennial tension between examining the agency of women and the constraining impacts of the systems and structures around them.The anthology works with the difficulty of reclaiming history, discussing women's activism in educational institutions and reforms therein, and particularly the naming of these projects as feminist projects.In the introduction, Aiston writes that the book operates with a broad definition of feminism, which "encompass[es] women who have improved the condition of fellow women through their activism" (p.5).Indeed, the book spans chapters that discuss Mary Wollstonecraft's educational history-often thought to be the author of one of the first feminist texts-to a postfeminist look at feminism in 1963 America through the publishing of The Feminine Mystique and the 1963 Presidential Commission on the Status of Women, to the ways in which femininity and womanhood were deployed in Şükûfe Nihal's campaign to demonstrate the importance of women to the Turkish state.Each of these chapters navigates the unclear waters of naming women as feminists who would not have identified as such, as well as with naming particular historical moments as feminist.In particular, the final two chapters seem to be at odds.The first of these chapters suggests that readers look at 1963 feminism from a postfeminist perspective, and Ellis and Mitchell end the collection with a recounting of the
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".