Bibliographic record
Abstract
Abstract The success story of Chinese manufacturing in the last quarter‐century is inextricably meshed with the story of migrant workers toiling for subsistence wages to produce goods for export. Indeed, the total stock of rural migrant labor, estimated to be about 155 million in 2010 (Cai et al. 2011: 18), has been the backbone of China's export industry since the mid‐1990s. In export centers such as Shenzhen and Dongguan, migrant labor accounted for the great majority (70–80%) of the labor force in the early years of the 21st century (Chan 2007). Rural–urban migration has also played a very important part in China's recent epic urbanization. In the 30 years since 1979 China's urban population has grown by about 440 million to 622 million in 2009 (Chan 2010c). Of the 440 million based on de facto urban population counts, the increase of about 340 million was attributable to net migration and urban reclassification (for estimates of components and definitions, see Chan & Hu 2003; Chan 2007). The latest urban population count, based on the 2010 census, was 666 million in November 2010 (NBS 2011). Even if only half of that increase was due to migration, the volume of rural–urban migration in such a short period is likely the largest in human history. The latest urban population count, based on the 2010 census, was 666 million in November 2010 (NBS 2011).
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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.005 |
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".