Explaining the sharp decline in birth rates in Canada and the United States in 2020
Bibliographic record
Abstract
Birth rates in Canada and the United States declined sharply in March 2020 and deviated from historical trends. This decline was absent in similarly developed European countries. We argue that the selective decline was driven by incoming individuals, who would have traveled from abroad and given birth in Canada and the United States had there been no travel restrictions during the COVID-19 pandemic. Furthermore, by leveraging data from periods before and during the COVID-19 travel restrictions, we quantified the extent of births by incoming individuals. In an interrupted time series analysis, the expected number of such births in Canada was 970 per month (95% CI, 710-1200), which is 3.2% of all births in the country. The corresponding estimate for the United States was 6700 per month (95% CI, 3400-10 000), which is 2.2% of all births. A secondary difference-in-differences analysis gave similar estimates, at 2.8% and 3.4% for Canada and the United States, respectively. Our study reveals the extent of births by recent international arrivals, which hitherto has been unknown and infeasible to study.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".