Retrofitting urbanising villages and tenants’ residential satisfaction: a case study of Shenzhen, China
Bibliographic record
Abstract
Since the 1970s, an inclusive slum upgrading strategy focused on perceived neighbourhood environment enhancement has gained prominence in the Global South. In China, recent years have witnessed a new inclusive instance of informal housing governance, namely retrofitting urbanising villages, which focuses on both housing characteristics and neighbourhood environments. This paper presents case studies of four urbanising villages in Shenzhen, China. The empirical analysis shows that the impact of the current retrofitting on tenants’ residential satisfaction primarily stems from enhancements in housing characteristics, while the improvement of neighbourhood environments is relatively inadequate. Further analysis suggests that among the limited improvements, concentrated retrofitting, as opposed to dispersed retrofitting, can more effectively enhance the neighbourhood environment, thereby increasing tenants’ residential satisfaction. For better inclusive governance and increased residential satisfaction, we suggest a concentrated retrofitting mode supported by collective actions and continuing improvement of both housing characteristics and neighbourhood environments. The findings also offer insights for slum upgrading in the Global South.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".