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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".