Filling in the gaps: examining the prevalence of Black homelessness in Canada
Bibliographic record
Abstract
Purpose Due to ongoing inequities in the social determinants of health and systemic barriers, homelessness continues to be a significant concern that disproportionately impacts racialized communities. Despite constituting a small proportion of the population, Black individuals are over-represented among people experiencing homelessness in many Canadian cities. However, although Black homelessness in Canada is a pressing issue, it has received limited attention in the academic literature. The purpose of this paper is to examine the reported prevalence of Black homelessness across Canada. Design/methodology/approach By consulting enumerations from 61 designated communities that participated in the 2018 Nationally Coordinated Point-in-Time Count and two regional repositories – one for homeless counts supported by the government of British Columbia and another from the Rural Development Network – this paper reports on the scale and scope of Black homelessness across Canada. Findings Significantly, these reports demonstrate that Black people are over-represented among those experiencing homelessness compared to local and national populations. These enumerations also demonstrate significant gaps in the reporting of Black homelessness and inadequate nuance in data collection methods, which limit the ability of respondents to describe their identity beyond “Black.” Originality/value This research provides an unprecedented examination of Black homelessness across Canada and concludes with recommendations to expand knowledge on this important and under-researched issue, provide suggestions for future iterations of homeless enumerations and facilitate the development of inclusive housing policy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".