China’s demographic prospects to 2040 and their implications: an overview
Bibliographic record
Abstract
China’s population prospects over the decades ahead are largely shaped by pro-longed sub-replacement childbearing, likely to have been in effect for half a century by 2040. China’s population is on track to peak in the coming decade and to decline at an accelerating pace thereafter. Between 2015 and 2040, China’s population aged 50 and older is on course to increase by roughly one-quarter of a billion people; the under-50 population is set to decline by a roughly comparable magnitude. China is set to experience an extraordinarily rapid surge of population aging, with especially explosive population growth for the 65-plus group, even as its working-age population (conventionally defined as the age 15–64 group) progressively shrinks. Additionally, a number of demographic changes underway now constitute “wild cards” for China’s future: including (1) the impending “marriage squeeze” due to abnormal sex ratios at birth from the one-child policy era; (2) the problem of mass urbanisation under a system that consigns migrants in urban areas to an officially inferior status; and (3) the revolutionary changes in the Chinese family structure, which portend a dramatic departure from previous arrangements on which Chinese society and economy depended.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".