Bibliographic record
Abstract
During the last two and a half decades, China has witnessed demographic change of historic proportions. It has transformed from a “demographic transitional” society, one where reductions in mortality led to rapid population growth and subsequent reductions in fertility led to slower population growth, to a “post transitional” society, where life expectancy has reached new heights, fertility has declined to below-replacement level, and rapid population aging is on the horizon. In the not-too-distant future – in a matter of a few decades – China's population will start to shrink, an unprecedented demographic turn in its history in the absence of massive wars, epidemics, and famines. In this process, China will also lose its position as the most populous country in the world. Demographic changes in China are monumental for reasons in addition to the shifts in traditional demographic parameters – mortality, fertility, population growth rate, and age structure. During its economic transitions of the last two and a half decades, China has also seen migration and urbanization processes that are unprecedented in world history for their sheer magnitudes. Population redistribution is inextricably tied to the broad social and economic transitions that China has undergone, and at the same time, it has also shaped important underlying conditions, as opportunities and constraints, for China's economic transition. At the start of China's economic reform in the late 1970s, the post-Mao Chinese leadership established population control as one of its top policy priorities.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".