China's new age floating population: Talent workers and drifting elders
Bibliographic record
Abstract
Talent workers are becoming a critical factor in the urbanization of China. Government programs encourage the attraction of these skilled and university educated workers by providing subsidized housing. Talent workers are responding to these incentives, yet many are not settling in the urban communities that woo them. This micro-scale case study of talent worker housing in Shenzhen explores some aspects of the lived experience of these “floating” talent workers. As a well-paid and upwardly mobile component of the floating population, talent workers are typically co-resident with their spouses and dependent children. However, a third generation is commonly present in these urban extended family households. Parents and in-laws of talent workers provide grandchild care and as such, they exemplify the “drifting elderly,” a newly identified phenomenon in China's cities. Interview evidence shows that about one half of young talent workers and virtually all of the drifting elderly constitute a new age floating population, challenging traditional conceptions of the floating population as an urban underclass. Despite the provision of subsidized housing intended to foster the retention of talent workers in Shenzhen, many are not committed to staying in their community and have no interest in attaining local urban hukou status.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".