Bibliographic record
Abstract
Introduction PART I: CROSSING INTERDISCIPLINARY BOUNDARIES 1. From the Ground Up Cynthia Enloe 2. My Women's Studies Journey Maithreyi Krishnaraj 3. Reclaiming my Education: A Passage to Consciousness Nawar Al-Hassan Golley 4. Oppositional Imaginations: Multiple Lineages of feminist Scholarship Uma Chakravarti 5. From Feminist Activist to Professor Drude Dahlerup 6. My Tryst with Women's Studies Rekha Pande PART II: ARTICULATING REGIONAL EXPERIENCES 7. Being a Woman and doing Gender in Sweden Anita Nyberg 8. Mainstreaming Women's Studies in Higher Education - The Case of Vietnam Thai thi Ngoc Du 9. My Journey in Chinese Women's Studies Paul S. Ropp 10. Feminism and Women's Studies in Japan Ronni Alexander 11. Working on the History of Chinese Women: My Story Clara Wing-chung Ho 12. Feminism, Women's Studies and the Women's Movement in Canada: Two Canadian Perspectives Marilyn Porter and Caroline Andrew PART III: TRANSNATIONAL AND DIASPORIC EXPERIENCES 13. Learning from Women for Women Tahera Aftab 14. My Life before and after Women's Studies- Insook Myongji University 15. A Personal Odyssey toward 'Feminist Curiosity' Hulya Adak 16. The Personal is (still) Political: Feminist Reflections on a Transformative Journey Simona Sharoni 17. State Feminism, Feminists and Women's Studies in Sweden Mona Eliasson 18. My Life and Women's Studies Geraldine Forbes
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.028 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".