Study of Global and Regional Fertility Trends and Factors Driving
Bibliographic record
Abstract
This study examines the main drivers of fertility decline in China, South Korea and Singapore, including economic pressures, sociocultural changes, and limitations in policy implementation. Despite a variety of policies implemented by the governments of the three countries to encourage fertility, high cost of living, employment pressures, and changing attitudes toward marriage and parenthood have kept fertility low for a long time. Fertility decline has already had far-reaching impacts on the demographic structure, labor markets and social security systems, and without effective measures, it may exacerbate the problems of slowing economic growth, labor shortages and increasing pension burdens in the future. In addition, low fertility may lead to smaller families, changes in intergenerational relations and a decline in social dynamics, further affecting social stability. To address this challenge, countries should adopt more integrated policies, including reducing childcare costs, optimizing the workplace environment, promoting gender equality, and inducing social attitudinal changes, to create a more child-friendly environment for sustainable socio-economic development.
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 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 | 0.000 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".