Bibliographic record
Abstract
The research for this book began in my youth during the years I lived in Peshawar.I owe tremendous gratitude to my father, who raised five opinionated daughters and encouraged us to ask questions and persist despite challenges.He was a strong, honorable man who was not afraid of raising strong women.I wish he were alive to read this book, and the hope that I will meet him again allows me to make this world mine in the moment.My mother taught me reading and loved me through the many struggles of life.The questions I ask in this book arise from the contested but loving relations I have shared with my family, especially my four sisters, Anita, Shazia, Bushra, and Lyla.In Afghanistan, I am thankful to all the women, most of whom must remain anonymous, who allowed me to become a part of their lives at the khana-yi aman.I thank Omar Sharifi for his friendship and support in all stages of the project in Afghanistan and New York.I thank the many friends, relatives, and colleagues who helped me along the way: Ai-
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.306 | 0.200 |
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".