Bibliographic record
Abstract
Along with its rapid socioeconomic development over the past few decades, China experienced profound demographic transitions, and has entered into a new stage of demographic development.In the past half-century, China has completed the demographic transition and has become a country with low population growth.China's 2010 census confirms the new demographic era, characterized by prolonged low fertility, persistently elevated sex ratios, rapid aging, massive urbanization, and widespread geographic redistribution (Cai 2013).The results of 2010 census confirm a series of demographic changes that had been largely foreseen by demographers and reveal few unexpected trends.Isabelle Attané and Baochang Gu's collection of essays titled Analysis China's Population: Social Change in New Demographic Era aims to address various defining patterns of China's demographic landscape in the early twenty-first century, some of which pose severe challenges to China's government.The 297-page collection includes 13 papers in three parts.In the first chapter, titled "China's demographic in a changing society: Old problem and new challenge," Isabelle Attané and Baochang Gu give a comprehensive review of demographic vicissitudes and social change in last few decades, and also a brief introduction of the 12 papers collected in the book.The six papers of the first part, entitled "China's low fertility: Facts and correlates," focus on concerns relating to recent fertility trends.In chapter 2, titled "China's low fertility: Evidence from the 2010 census", Zhigang Guo and Baochang Gu argue that the 2010 census reflects the true level of fertility which is far from adhering to the official TFR of 1.8.In Chapter 3, titled "Changing pattern of marriage and divorce in today's China," Jiehua Lu and Xiaofei Wang state that first marriage is increasingly delayed for both men and women, the age-specific proportions of unmarried people are growing, and divorce is now better accepted socially.In Chapter 4, titled "Education in China: Uneven progress," Qiang Ren and Ping Zhu indicate that China has achieved significant improvement in education, while progress is uneven and gaps remain, in particular between gender, ethnic groups, provinces, and place of residence.In Chapter 5, titled "The male surplus in China's marriage market: Reviews and prospects," Shuzhuo Li, Quanbao Jiang, and Marcus W. Feldman estimate the male surplus in the population and investigate the possible social and individual consequences of the male-biased sex structure.In Chapter 6 titled "Being a woman in China today: A demography of gender," Isabelle Attané draws up a socio-demographic inventory of the situation of Chinese women in demographic and socioeconomic transition.The three papers of the second part, entitled "Modernization, social change, and social segregation," focuses on various dimensions of social inequality that have emerged or grow more acute with the transformation of economic system.In chapter 7, titled "Are China's minority nationalities still on margin?",Dudley Poston and Qian Xiong conclude that Chinese minorities are socially different from the Han majority due to centuries of spatial segregation.In chapter 8, titled "Demographic and social impact of internal migration in China," Delia Davin focuses on rural-to-urban migration flows and their impact on age and sex structure, people left behind
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".