Addressing homelessness in a smaller Canadian city : community-engaged research with Vernon, B.C.
Bibliographic record
Abstract
Cities across Canada are experiencing an increase in homelessness and are struggling to keep up with the needs of the growing homeless population. Smaller cities are no exception to this trend. Canada is experiencing a homelessness crisis across the nation as a combined result of the federal government’s divestment of affordable and social housing, increased housing and rental prices, cutbacks in full-time and well-paying employment, and reduced investments in mental health supports throughout the country (Gaetz et al., 2016). Government policy changes and shifts in the economy have shortened the affordable housing supply and reduced rental subsidies and other supports for low-income populations (Dalton, 2009; Gaetz, 2010). In response to the growing issue of homelessness, the Federal Government initiated a homelessness strategy called Reaching Home that provides selected communities across Canada with funding to address homelessness (Government of Canada, 2020a). However, communities that are excluded from this program are finding it increasingly challenging to address and reduce homelessness. Vernon, British Columbia (Vernon) is one of the many cities struggling to deal with homelessness without supports from the Federal Government. Concomitant to the exclusion of smaller cities, funding research has also prioritized homelessness in larger Canadian cities such as Toronto and Vancouver (Doberstein, 2012; Piat et al., 2015; To et al., 2016). As a result, little is known at the academic level around how smaller Canadian cities address homelessness with limited funding and capacity. Using a community-engaged methodology, I have partnered with Vernon to examine the ways in which the city addresses homelessness with minimal supports from the Federal Government. I hope that this research will put Vernon and other smaller Canadian cities on the academic radar, drawing attention to the challenges faced by smaller communities throughout the homelessness crisis. The goal of this research is to fill the gap in knowledge within academia around how smaller Canadian cities alleviate homelessness with limited funding and resources.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".