Exploring the influence of socio-demographic factors on fertility decisions among women in Canada
Bibliographic record
Abstract
The global decline in fertility rates has drawn the attention of economists, researchers, and policymakers in recent decades. Given the economic and social implications of population changes, it is important to analyze what underlying factors have influenced fertility decisions over time, and what is behind the sharp change in the decision-making process regarding family size, and hence, population growth. The present paper analyzes the influence of different socio-demographic factors on two main fertility decisions: the number of children and the age of women at first child. The analysis is done employing data from the Canadian Social Survey for the years 2006, 2011, and 2017, proposing an estimation with an ordered logit model framework and accounting for possible cohort effects by incorporating birth-control variables. Main findings suggest that higher levels of education, urbanity, certain regions, and being religiously unaffiliated are associated with a higher likelihood of reporting fewer amount of children. These results also contribute to reporting a first child at an older age, except for the variable that captures religious attendance, which is associated with a higher likelihood of reporting a first child at an older age despite the fact that attending religious events is linked to a higher amount of children.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".