Stagnation or upward mobility? The influence of achieved and ascribed factors on the housing careers of residents in Shanghai
Bibliographic record
Abstract
Research on housing inequality has predominantly focused on the differentiation of housing states at specific time points in contemporary China, with minimal attention given to understanding how individuals’ housing states evolve throughout their life course. This study, based on a retrospective survey conducted in Shanghai between 2018 and 2019, investigates the progression of residents’ housing careers and the influence of both ascribed and achieved factors. The findings reveal that individuals from privileged families are secured with advantages and even already become homeowners at the outset of their housing careers. In contrast, the influence of achieved factors takes time to manifest; for instance, educational attainment may not be determinant initially but positively correlates with upward mobility in a later stage, particularly among younger cohorts. Furthermore, it has been found permanent migrants who transferred to local hukou perform well in achieving upward housing mobility. Despite lacking the advantages of ascribed factors, they manage to catch up and even surpass locals through their proactive efforts. This study underscores the significance of adopting a temporal perspective in comprehending housing inequality and also emphasizes the dynamic influence of both ascribed and achieved factors on individuals’ housing outcomes in a restructured housing market.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".