Decomposing changes in first birth trends: Quantum, timing, or variance
Bibliographic record
Abstract
In high-income countries, women and men born since the 1940s have delayed the birth of their first child, more of them have remained childless, and the timing of the first birth has become more diverse in these cohorts. The interaction between these three trends makes the research on first birth patterns more complex. This study has two main aims: (1) we introduce an alternative index, Expected Years Without Children (EYWC), to quantify changes in first birth behaviour; and (2) we decompose the changes in EYWC over time into three effects: remaining permanently childless, postponing the first birth, and the expansion of the standard deviation of the mean age at first birth. Using data from the Human Fertility Database, EYWC is calculated to illustrate time trends among women born in the 1910s–1960s in eight countries with longer series of data on cohort first birth trends: Canada, the Czech Republic, Japan, the Netherlands, Norway, Portugal, Sweden, and the United States. Our decomposition shows that the changes in EYWC are mainly attributable to postponement in North America and northern Europe, whereas these changes are largely due to increasing shares of women remaining childless in Japan and Portugal.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".