Are the Millennials Getting Less Married?
Bibliographic record
Abstract
The study investigates the causal link between the Millennials (ML) Population (18-37 year age) and the Marriage Rate (MR) (married population/total population) for the countries of France, Germany, Italy, Netherlands, Spain, and the United Kingdom (UK) by using the bootstrap causality test. The findings suggest that ML population has a significant negative impact on MR in Italy and the Netherlands, while MR has a significant negative impact on ML population in Spain. Besides, the System Generalized Method of Moment Regression (SGMM) is conducted to release the effects of the Divorce Rate (DR), Education Attainment (EA), Globalization (GB), Social Protection (SP), Secularization (SEC), House Prices (HP), Financial Crisis (FC), and Working Population of women (WP) variables on MR and ML population. Likewise, the outcomes display that these are the leading factors of explaining ML population. Our results support the two-period model of Peters (1986), which states that MR is the combination of the economic, social, and religious elements and has important policy implications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".