Changing Seasonal Variation in Births by Sociodemographic Factors: A Population-Based Register Study
Bibliographic record
Abstract
<b>Abstract:</b> Seasonality of births is in some populations strongly influenced by sociodemographic factors. In this study, we analyse the impact of mothers’ sociodemographic characteristics for the seasonal variation in 7,710,955 live births in Sweden between 1940 and 2012. During 1940-1999, Swedish birth rates showed the typical seasonal variation with high numbers of births during the spring, and low numbers of births during the last quarter of the year. However, during the twenty-first century, the seasonal variation in fertility declined so that only minor variation in birth rates between February and September remains. Still, the pattern of low birth rates at the end of the year remains and has even become more pronounced in recent decades. The roles of maternal education, mother’s birth country, parity, and instances where the mother has re-partnered between subsequent births changed during the second half of the twentieth century. The study underlines that in a society with low fertility and efficient birth control, active choices and behaviours associated with an individual’s sociodemographic characteristics tend to matter more for the seasonal timing of childbearing than environmental factors related to the physiological ability to reproduce and cultural-behavioural factors related to the frequency of intercourse.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".