Exploring Homelessness in Small-To-Mid-Sized and Large Canadian Cities: An Analysis of the Canadian Housing Survey
Bibliographic record
Abstract
Most research on homelessness in Canada has been undertaken in large cities, such as Toronto, Vancouver, and Montreal. This paper will explore levels of homelessness in small-to-mid-sized Canadian cities (50-500,000) compared to levels of homelessness in large cities/Census Metropolitan Areas (CMAs) with populations over 500,000. As part of a larger project, which is studying homelessness in three small-mid-sized Ontario cities, which is mainly based on qualitative methods, this article will analyze data from the Canadian Housing Survey for the years 2018 and 2021. The paper will focus on two themes. First, we will compare prevalence rates of homelessness in mid-size cities with rates in large Canadian CMAs. This will be followed by a bi-variate analysis exploring factors associated with homelessness in these two geographical groupings. The analysis will conclude with a multi-variate analysis assessing if the demographic characteristics of the respondents (gender, sexual orientation, age, education, and ethno-racial identity) predict a respondent’s history of homelessness, and whether or not these relationships differ between respondents living in Canadian CMAs compared to respondents residing in small-mid-size cities.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".