Bibliographic record
Abstract
Over the past quarter century the sex ratio at birth (SRB) has risen above natural levels in a number of countries, mostly in Asia. This rise has been made possible in populations with strong son preference by the increasing availability of safe, effective, and inexpensive technologies to determine the sex of a fetus and to end unwanted pregnancies. This article documents levels and trends in the sex ratio at birth, in preferences for male offspring (using information on desired number of girls and boys), and in the implementation of these preferences. DHS surveys from 61 countries in Africa, Asia, and Latin America and for Indian states are the main source of data. A comparison of desired with actual SRBs finds large gaps in most populations, implying a substantial pent‐up demand for male offspring and the technology to implement this preference. Two types of actions to implement preferences are considered: the practice of contraception to stop childbearing after the desired number of sons has been born and the use of sex‐selective abortion to avoid female births. The second part of the article discusses factors that could influence the SRB, including the promotion of gender equality, and the implications of these factors for future trends.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".