Sex-selective Infanticide and the “Missing Females” in China and India
Bibliographic record
Abstract
Awarded the Adele Mellen Prize for Distinguished Contribution to Scholarship This book contains a collection of twelve essays about the practice of infanticide in different parts of the world and written by women from different academic disciplines, with an introductory chapter that analyzes the origins and development of scholarship on this topic. The book’s essays are divided into four parts that open with brief introductions. Two of these sections are based on common themes of infanticide, and the other two on the applications of similar methodologies. Part one contains essays that highlight the persistence of race and inequality in shaping the context of infanticide in such diverse terrains as the Caribbean, Australia, and the American South. The second section demonstrates how governments in England, Canada, and the Soviet Union used their authority to control women’s behavior by instituting policies they thought would deter women from committing infanticide. The last two sections contain a variety of essays about infanticide in Africa and the Americas, but are similar in applying the case study method of analysis. The final part demonstrates the effectiveness of using sex ratios and computer data analysis to study infanticide in Asia and western Europe. The book concludes with a lengthy, multidisciplinary bibliography of the infanticide literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".