UNPACKING THE DIFFERENTIAL IMPACT OF FAMILY PLANNING POLICIES IN CHINA: ANALYSIS OF PARITY PROGRESSION RATIOS FROM RETROSPECTIVE BIRTH HISTORY DATA, 1971–2005
Bibliographic record
Abstract
Although China's family planning programme is often referred to in the singular, most notably the One-Child policy, in reality there have been a number of different policies in place simultaneously, targeted at different sub-populations characterized by region and socioeconomic conditions. This study attempted to systematically assess the differential impact of China's family planning programmes over the past 40 years. The contribution of Parity Progression Ratios to fertility change among different sub-populations exposed to various family planning policies over time was assessed. Cross-sectional birth history data from six consecutive rounds of nationally representative population and family planning surveys from the early 1970s until the mid-2000s were used, covering all geographical regions of China. Four sub-populations exposed to differential family planning regimes were identified. The analyses provide compelling evidence of the influential role of family planning policies in reducing higher Parity Progression Ratios across different sub-populations, particularly in urban China where fertility dropped to replacement level even before the implementation of the One-Child policy. The prevailing socioeconomic conditions in turn have been instrumental in adapting and accelerating family planning policy responses to reducing fertility levels across China.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".