2.4-O2Do son-biased sex ratios at birth persist among second generation South Asian women in Ontario, Canada?
Bibliographic record
Abstract
Introduction: Previous research in Ontario has found that first generation Indian women with two previous daughters give birth to half the expected number of daughters compared to sons at the third birth. Biased sex ratios were likely facilitated by sex-selective abortion. Our objective was to examine whether son-bias persists among second generation South Asian (SA) women in Ontario. Methods: We analysed births to immigrant and Canadian-born SA women as well as the general population who gave birth in Ontario between 1991 and 2014. SA women were identified using a comprehensive list of exclusively SA surnames [positive predictive value=89.3%; sensitivity=50.4%]. SA immigrants were identified using a combination of this list and an official immigration database (1985-2012). Second generation SA women excluded immigrants and was further restricted to those who were born in Ontario. Male to female (M:F) ratios and 95% confidence intervals (95% CI) were calculated according to the sex of previous live births for each group and further stratified by those who did and did not have an abortion since the previous live birth. Results: For births to SA immigrants (n = 36,816, of whom 65% were born in India), the M:F ratio at the third birth among women with two previous daughters was 1.42 (95% CI 1.25-1.62) for women with no previous abortion and 2.46 (95% CI 1.93-3.12) for women with at least one previous abortion since the second birth (n = 325). Biased sex ratios among births to second generation SA women (n = 10,427) were evident only among those with two previous daughters and at least one previous abortion (n = 46) [2.80 (95% CI 1.36-5.76)]. Main message: Son-biased M:F ratios generally do not persist among second generation SA women with the exception of a minority of women with two previous daughters with at least one abortion since their second birth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".