Low fertility and fertility policies in the Asia-Pacific region
Bibliographic record
Abstract
Declining fertility is an increasing global trend. In many low fertility contexts, people are having fewer children then they want, and these unfulfilled fertility desires have been associated with wider socio-economic changes in education and labour force participation and conflicting and often contradictory expectations of women at home and at work. The right to determine if, when and how one has children is enshrined in international law yet many policies responses to low fertility fail to meet these standards. This paper summarizes why people in the Asia-Pacific region are having fewer children than they desire, and the range of policy responses, particularly those that make life easier for working parents. This raises two important points. First, we need to contend to the gender dynamics that underpin this in the region, despite gradual changes in women's roles, they are still seen as "caregivers" and undertake a disproportionate amount of unpaid care work, often having to lean-out of their employment, and/or face gender discrimination in the workplace. Second, the "emergency" of low fertility arises from complex social and economic conditions that cannot be solved by population policies solely focused on making babies.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".