Bibliographic record
Abstract
My research and this book would not have been possible without the cooperation of the thirty women who took time out of their busy lives (from the autumn of 1999 to the summer of 2000) to tell me about their activist and caring work.I first want to express my thanks and appreciation to them.I owe thanks to many other people for their support and assistance at different stages of the research and writing process.I am most indebted to Catriona Sandilands whose mentorship, incisive and rigorous criticism, and commitment to conversation over the past nearly ten years are the reasons I was able to see this project through to fruition.Her book The Good-Natured Feminist: Ecofeminism and the Quest for Democracy (1999) was an inspiration for my research and continues to inform my thinking in important ways.Ilan Kapoor, Lorraine Code, and Margrit Eichler each helped guide me through the research and writing process and gave excellent advice on how to work through and present my various conflicting arguments.Lorraine
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.205 | 0.139 |
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".