Transition and challenge China's population at the beginning of the 21st century
Bibliographic record
Abstract
With the largest population in the world, China has experienced significant demographic, social, and economic changes in recent decades. Extraordinary demographic changes took place in China in the second half of the twentieth century having wide-ranging consequences. This book, written by a group of leading experts, examines these profound changes in an effort to understand their long term impact and provide an up-to-date account of China's demographic reality. The volume provides a comprehensive and authoritative analysis of a wide range of issues such as China's unprecedented family planning program, the impact of falling birth rates coupled with increasing life expectancy, changes in marriage patterns, and increasing rural-urban migration. Anyone who is interested in China and its recent demographic changes will benefit from the rich materials and thorough analysis provided in this book. Contributors to this volume - Isabelle Attane, Institut National d'Etudes Demographiques Judith Banister, Global Demographics, New York Yong Cai, University of Utah, Salt Lake City John Caldwell, The Austrialian National University Xingshan Cao, University of Toronto, Mississauga Wei Chen, People's University of China, Beijing Baochang Gu, People's University of China, Beijing Fei Guo, Macquarie University, Sydney Zhigang Guo, Peking University, Beijing Edward Jow-Ching Tu, The Hong Kong University of Science and Technology William Lavely, University of Washington, Seattle Zai Liang, State University of New York at Albany Andrew Mason, University of Hawaii, Honolulu Kenneth Roberts, Southwestern University, Georgetown Thomas Scharping, Universitat Koln, Germany Feng Wang, University of California, Irvine Xin Yuan, Nankai University, China Guangyu Zhang, Flinders University, Adelaide Weiguo Zhang, University of Toronto, Mississauga Xia Zhang, The Hong Kong University of Science and Technology Zhongwei Zhao, The Australian National University
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".