Outcome of the 2016 United States presidential election and the subsequent sex ratio at birth in Canada: an ecological study
Bibliographic record
Abstract
OBJECTIVES: The sex ratio at birth (proportion of boys to girls) generally shows slight male preponderance but may decrease in response to societal stressors. Discrete adverse events such as terrorist attacks and disasters typically lead to a temporary decline in the sex ratio 3-5 months later, followed by resolution over around 5 months thereafter. We hypothesised that the unexpected outcome of the 2016 US presidential election may have been a societal stressor for liberal-leaning populations and thereby precipitated such an effect on the sex ratio in Canada. DESIGN: Ecological study. SETTING: Administrative data for Ontario (Canada's most populous province). PARTICIPANTS: All births in Ontario from April 2010 to Oct 2017 inclusive (n=1 079 758). PRIMARY AND SECONDARY OUTCOME MEASURES: We determined the sex ratio at birth in Ontario for each month from April 2010 to October 2017 and performed segmented regression analysis to evaluate the seasonally adjusted sex ratio for the following three time periods: before the November 2016 election; after the election to before the anticipated impact; and from anticipated impact to 5 months thereafter. RESULTS: In the 12 months following the election, the lowest sex ratio occurred in March 2017 (4 months post election). Compared with the preceding months, the sex ratio was lower in the 5 months from March to July 2017 (p=0.02) during which time it was rising (p=0.01), reflecting recovery from the nadir. Both effects were seen in liberal-leaning regions of Ontario (lower sex ratio (p=0.006) and recovery (p=0.002) in March-July 2017) but not in conservative-leaning areas (p=0.12 and p=0.49, respectively). CONCLUSION: The 2016 US presidential election preceded a temporary reduction in the sex ratio at birth in Canada, with the time course of changes therein matching the characteristic pattern of a discrete societal stressor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".