High-rise apartment quality evaluation and related demographic factors: lesson from RentSafeTO programme
Bibliographic record
Abstract
With the rapid increase in urbanization and the number of residents living in high-rise apartment buildings, the quality of living environments in terms of the facility, safety and hygiene of high-rise housing has become an important topic. Although numerous studies have investigated occupant satisfaction through subjective assessment, only few studies have used objective assessment methods, such as expert evaluation, to elucidate the quality of high-rise apartments and the related occupancy factors. According to the dataset from Toronto's RentSafeTO programme, which provides the results for 9928 high-rise apartments evaluated using 20 quality indicators, this study conducted a factor analysis and identified two main factors for assessing high-rise housing: building structure and building facilities. Furthermore, this study used multiple regression models and census data to analyse the housing quality at the regional level. The results of social housing and private housing differed. Labour force attributes, education, immigration and ethnic origin significantly affected the quality of private housing. The results provide important directions for the post-occupancy evaluation of high-rise apartments. In addition, demographic factors significantly affected residential quality. This study provides a basis for the government to formulate equal and unbiased support for high-rise building maintenance and management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".