Determinants of housing moving intention of urban low-income residents in China: Life-cycle, lived experience, and place
Bibliographic record
Abstract
This paper examines the residential mobility of urban low-income residents in two Chinese cities using survey data. Contrary to previous studies, our results reveal a relatively low level of housing mobility among the urban poor. In exploring the determinants of low-income housing mobility, we employ both life-cycle and social-psychological theories, with particular attention to the role of place-based factors and their interactions with other variables. Our analysis uncovers a strong relationship between housing mobility and life-cycle variables, albeit with considerable heterogeneity among residents. Additionally, the lived experience of housing emerges as a significant predictor of mobility. Lastly, we find that the interplay of place and space exerts a profound and complex influence on this process, shaping moving intentions at various geographical scales and interacting with other factors to determine the housing mobility of urban low-income groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".