Housing Inequality and Subjective Well-being in Urban China——A Multilevel Analysis on CGSS Data
Bibliographic record
Abstract
Based on data from the Chinese General Social Survey 2005,we analyze the impact of housing inequality on subjective well being(SWB) on Hierarchical Linear Modeling in urban China.The results show that there is regional disparity of SWB.The number of the housing does have a strong positive effect on SWB,and there is aninverted-Uassociation between housing size and SWB.The relative inequality of housing such as the gap of the housing size between the average housing size and the per capital housing size of the province,has a strong negative effect on SWB.In addition,there is alsoinverted-Uassociation between Gini coefficient of housing of the province and the SWB,The critical point in Gini coefficient is found at 0.325,which means that SWB of the citizen increases with housing inequality when the coefficient is less than 0.325 but SWB decreases with housing inequality when it is larger than 0.325.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".