Social housing stigma in Toronto: Identifying asymmetries between stereotypes and statistical actualities of health, crime, and human capital
Bibliographic record
Abstract
Abstract Research on social housing stigma has proliferated due to growing concern over the effects of territorial stigmatization. The stereotyping of social housing as a site of ill‐health, criminality, and low human capital stems from empirically ambiguous narratives created and recirculated through popular modes (e.g., social media platforms, news coverage). This paper combines principal component analysis, k‐means cluster analysis, and geographic information systems to create and visualize clusters denoting different levels of health, crime, human capital, and dwelling composition in the city of Toronto, Canada. The quantitative research design allows for the identification of “asymmetries,” which are census tracts or neighbourhoods assigned to clusters indicative of high social housing density, and one of either sound health, low crime, or high human capital. The results reveal a spatial patterning of asymmetries in the inner city West End and Downtown, and in inner suburban North York, Etobicoke, and Scarborough. Overall, the paper illustrates the need to assess the empirical foundations of social housing stereotypes. Critically assessing stereotypes is important as they belie the rationale for social housing residents' living situations; pathologizes their identity, behaviour, and home; and generates public support for neoliberal solutions that displace long‐term residents from their communities .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".