Growth Without Mobility? Reproduction of Social Hierarchies in China’s Reform Era
Bibliographic record
Abstract
Over the past four decades, China has experienced unprecedented rapid economic growth, with surging output, accelerated urbanization, improved access to education, and technological innovation collectively driving the country toward modernization. An increasing number of young people have found that individual effort alone cannot overcome structural barriers to the resources. Social phenomena such as “poverty traps,” “exam fever,” “school district housing,” and “lying flat ideology” reflect the ongoing exacerbation of social inequality. This paper adopts Bourdieu's theory of capital and social reproduction as its analytical framework, focusing on the accumulation and transmission mechanisms of economic capital, cultural capital, and social capital across generations, and on how these mechanisms shape individuals' social mobility pathways. Through the combination of statistical data and regional case studies, this paper reveals that Chinese society is gradually evolving from a “mobile society” to a “stratified society.” Specifically, the imbalance in development between eastern, central, and western regions has caused structural fractures in mobility opportunities; the urban-rural dichotomy has led to a marked concentration of educational resources among the urban middle class and elite groups; and skyrocketing housing prices have emerged as a new barrier to mobility. Additionally, “relationship capital” often proves more critical than individual ability in the job market, further compressing the upward mobility space for the lower strata.
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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 | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".