Exploring the Population Implications of Male Preference When the Sex Probabilities at Birth Can Be Altered
Bibliographic record
Abstract
OBJECTIVE: The paper explores the population effects of male preference stopping rules and of alternative combinations of fertility rates and male-biased birth sex ratios. METHODS: The “laboratory” is a closed, stable population with five age groups and a dynamic process represented by a compact Leslie matrix. The new element is sex-selective abortion. We consider nine stopping rules, one with no male preference, two with male preference but no abortion, and six with male preference and the availability of abortion to achieve a desired number of male births. We calculate the probability distribution over the number of births and number of male births for each rule and work out the effects at the population level if the rule were adopted by all women bearing children. We then assess the impact of alternative combinations of fertility rates and male-biased sex ratios on the population. RESULTS: In the absence of sex-selective abortion, stopping rules generally have no effect on the male/female birth proportions in the population, although they can alter the fertility rate, age distribution, and rate of growth. When sex-selective abortion is introduced the effect on male/female proportions may be considerable, and other effects quite different as well. The contribution of this paper is the quantification of effects that might have been predictable in general but which require model-based calculations to see how large they could be. As the paper shows, they could in fact be very large; a population in which sex-selective abortion was widely practised could look quite different from what it would otherwise be.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".