DWELLING SATISFACTION OF DISABLED HOUSEHOLDS IN ONTARIO
Bibliographic record
Abstract
Housing stock remains overwhelmingly inaccessible to disabled people in Ontario. The primary method by which accessibility in the home is improved is through the installation of dwelling adaptations, which modify the layout or structure of parts of the home. Measuring the impact these adaptations have on various measures of a household’s dwelling satisfaction will help inform disability and housing legislation, two policy arenas that have seen renewed interest by both provincial and federal governments in recent years after decades of cutbacks to social supports for disabled people. Using the 2018 Canadian Housing Survey dataset collected by Statistics Canada, Ontario households were split up into three groups; households that need no adaptations, households that need and have adaptations, and households that need but do not have adaptations. Demographic profiles of each group were built and compared. Various measures of dwelling satisfaction were also compared. This was done using descriptive statistics, t-tests, and logistic regression. All households that require a dwelling adaptation had much higher rates of Core Housing Need, rent subsidy, and lived in non-market rental housing compared to non-disabled households. Only half of households requiring adaptations had them. Households that had the adaptations they required had statistically comparable rates of dwelling satisfaction measures to non-disabled households. Households without the adaptations they required were more likely to be women-led, more likely to be led by a young adult, and were more likely to feel unsafe in their home. Adaptations were found to significantly alleviate dissatisfaction with the condition, safety, and accessibility of the home, but adapted households remain in more precarious and substandard housing compared to non-disabled households.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".