Some theoretical and methodological comments on the impact of policies on fertility
Bibliographic record
Abstract
Contents: Debate Can policies enhance fertility in Europe? (Anne H. Gauthier and Dimiter Philipov) What should be the goal of population policies? Focus on "Balanced Human Capital Development" (Wolfgang Lutz) Some theoretical and methodological comments on the impact of policies on fertility (Anne H. Gauthier) "Can policies enhance fertility in Europe?" and questions beyond (Nikolai Botev) First, do no harm (William P. Butz) Refereed Articles What can fertility indicators tell us about pronatalist policy options? (John Bongaarts) Institutions and the transition to adulthood: Implications for fertility tempo in low-fertility settings (Ronald R. Rindfuss and Sarah R. Brauner-Otto) A review of policies and practices related to the "highest-low" fertility of Sweden (Gunnar Andersson) Fertility trends and differentials in the Nordic countries - Footprints of welfare policies and challenges on the road ahead (Marit Rønsen and Kari Skrede) The impact of the bonus at birth on reproductive behaviour in a lowest-low fertility context: Friuli-Venezia Giulia (Italy) from 1989 2005 (Giovanna Boccuzzo, Marcantonio Caltabiano, Gianpiero Dalla Zuanna, and Marzia Loghi) Data & Trends (non-refereed contributions) French family policy: long tradition and diversified measures (Ariane Pailhé, Clémentine Rossier, and Laurent Toulemon) Family policies in Europe: available databases and initial comparisons (Olivier Thévenon)
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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 | 0.031 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".