Sex Ratios at Birth Among Indian Immigrant Subgroups According to Time Spent in Canada
Bibliographic record
Abstract
OBJECTIVES: To examine whether son-biased male to female (M:F) ratios at birth among linguistically different subgroups of Indian immigrants vary according to duration of residence in Canada. METHODS: We analyzed a retrospective cohort of 46 834 live births to Indian-born mothers who gave birth in Canada between 1993 and 2014. The M:F ratio at birth was calculated according to the sex of previous live births and stratified by (1) time since immigration to Canada (<10 and ≥10 years) and (2) mother tongue (Punjabi, Gujarati, Hindi, and other). We estimated adjusted odds ratios (aORs) using multivariate logistic regression to assess the probability of having a male newborn with 5-year increases in duration of residence in Canada for each language group. ORs were adjusted for married status, knowledge of English/French, maternal education at arrival and age and neighbourhood income at delivery. RESULTS: Among all Indian immigrant women with two previous daughters, M:F ratios were higher than expected (1.92, 95% CI 1.73-2.12), particularly among those whose mother tongue was Punjabi (n = 25 287) (2.40, 95% CI 2.11-2.72) and Hindi (n = 7752) (1.63, 95% CI 1.05-2.52). M:F ratios did not diminish with longer duration in Canada (Punjabi 5-year aOR 1.03, 95% CI 0.81-1.31; Hindi 5-year aOR 0.94, 95% CI 0.42-2.17). CONCLUSION: Among the Punjabi and Hindi women with two previous daughters, longer duration of residence did not attenuate son-biased M:F ratios at the third birth. Gender equity promotion may focus on Punjabi- and Hindi-speaking Indian immigrant women regardless of how long they have lived in Canada.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".