Urban and rural homelessness in Northern Ontario: an Indigenous lens
Bibliographic record
Abstract
Indigenous Peoples are at a higher risk of experiencing homelessness in Canada than non-Indigenous Peoples. As a result, there is a disproportionate representation of Indigenous Peoples in the homeless population. Studies have been conducted in order to identify what services are needed for people living with homelessness in Canada. However, these have failed to include an Indigenous specific focus and have not included the perspectives of Indigenous people with lived experiences. Furthermore, the majority of previous studies explore homelessness in large urban settings and seldom focus on rural or Northern Ontario. A secondary analysis on a photovoice study completed in 2014 on homelessness in Northern Ontario was done to highlight the unique experiences and needs of the Indigenous participants from the previous study. This research focused on health impacts and service provision requirements surrounding Indigenous homelessness in four communities in Northern Ontario. A literature review was also conducted in order to explore the laws and policies surrounding the rights and legal responsibility for services in relation to UNDRIP and the TRC. All of the photovoice data represents lived homelessness or hidden homelessness experience, from the perspectives of Indigenous participants. These are their voices.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".