Counting the Undercounted: Enumerating Rural Homelessness in Canada
Bibliographic record
Abstract
Until recently, homelessness in Canada was largely considered to be an urban phenomenon. This assumption has been reinforced by homelessness interventions that primarily focus on urban areas. The past decade has seen a steady increase in research and reports on rural homelessness, using primarily qualitative methods. Recently, there have been some efforts to develop enumeration methods to measure and describe the scale and scope of rural homelessness. These enumerations have resulted in unprecedented availability of quantitative data on the number and characteristics of people experiencing homelessness in rural Canada. In this article we report on research which collected and analyzed data from Canadian rural homelessness enumerations. Significantly, these reports show per capita rates of homelessness in rural communities that are higher than those seen in Canada’s largest urban centres. These enumeration reports also show that a significant percentage of persons experiencing homelessness (PEH) in rural Canada are unsheltered and fall into the category of absolute homelessness. This research provides a snapshot of rural homelessness that is contrary to the dominant narrative of predominately “hidden homelessness” in rural communities. We suggest adjustments to policy and funding of homelessness programs that consider this evolving knowledge about the scale and scope of homelessness in rural Canada.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".