The impacts of housing factors on deprivation in a world city: The case of Hong Kong
Bibliographic record
Abstract
Abstract Hong Kong is a typical example of a world city that faces escalating poverty and housing problems. Problems related to housing are crucial in determining deprivation. By means of hierarchical linear regression on a representative survey of Hong Kong residents in 2014, this study examines the impacts of household income and housing factors on the deprivation of residents in Hong Kong. The study indicates that income level has a crucial effect on the deprivation level of households; whereas housing cost per capita, living area per capita, and living quarter problems significantly influence deprivation. A small interacting effect exists between household income and housing factors, which do not influence the independent effects of living area per capita and living quarter problems on deprivation. For the public rental housing residents, only the effect of living quarter problem on deprivation is significant, whereas for private rental housing residents, living area per capita and living quarter problem have a significant effect. Among all the models, housing expense per capita is a significant factor only in model for overcrowded households. The study recommends that improving the maintenance and renovation schemes for public and private housing with poor living environment is a good strategy to improve housing conditions and deprivation. The study suggests that anti‐poverty policies must consider strategies and measures that can improve the housing factors, including housing expenses, living density and living quarter maintenance problems, especially for those residents with high living density, such as those living in bed spaces, cubicles, and subdivided flats.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".