Sex ratios among Canadian liveborn infants of mothers from different countries
Bibliographic record
Abstract
BACKGROUND: There has been much discussion about whether female feticide occurs in certain immigrant groups in Canada. We examined data on live births in Ontario and compared sex ratios in different groups according to the mother's country or region of birth and parity. METHODS: We completed a population-based study of 766,688 singleton live births between 2002 and 2007. We used birth records provided by Ontario Vital Statistics for live births in the province between 23 and 41 weeks' gestation. We categorized each newborn according to the mother's country or region of birth, namely Canada (n = 486,599), Europe (n = 58,505), South Korea (n = 3663), China (n = 23,818), Philippines (n = 15,367), rest of East Asia (n = 18,971), Pakistan (n = 18,018), India (n = 31,978), rest of South Asia (n = 20,695) and other countries (n = 89,074). We calculated male:female ratios and 95% confidence intervals (CIs) for all live births by these regions and stratified them by maternal parity at the time of delivery (0, 1, 2 or ≥ 3). RESULTS: Among infants of nulliparous women, the male:female ratio was about 1.05 overall. As parity increased, the ratio remained unchanged among infants of Canadian-born women. In contrast, the male:female ratio was significantly higher among infants of primiparous women born in South Korea (1.20, 95% CI 1.09-1.34) and India (1.11, 95% CI 1.07-1.15) than among infants of Canadian-born primiparous women. Among multiparous women, those born in India were significantly more likely than Canadian-born women to have a male infant (parity 2, ratio 1.36, 95% CI 1.27-1.46; parity ≥ 3, ratio 1.25, 95% CI 1.09-1.43). INTERPRETATION: Our study of male:female ratios in Ontario showed that multiparous women born in India were significantly more likely than multiparous women born in Canada to have a male infant.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".