Canadian homeless mobilities: Tracing the inter‐regional movements of At Home/Chez Soi participants
Bibliographic record
Abstract
People experiencing homelessness are simultaneously socially and physically mobile. Individuals move through periods of housing stability and houselessness and varying degrees of financial (in)stability, and between different geographic spaces. Research concerning homeless mobilities emphasizes moves within cities and reveals seven factors deserving attention: housing; labour markets; social, health, and justice services; personal health; the attributes of different places; interpersonal networks; and how mobility is socially differentiated. However, the extent to which these factors shape homelessness and inter‐regional mobilities is unclear. Addressing this gap, I explore 612 people's moves using data collected from five Canadian cities. By analyzing participants' inter‐regional moves over ten years, I identify ten themes of homeless inter‐regional mobility in Canada including: interpersonal networks, the attributes of different places, labour markets and personal finances, the use of movement for personal growth, health and social services, residential mobilities, legal and health institutions, substance abuse and dependence, personal security, and travel. I find that the structures, institutions, resources, and personal experiences that produce homelessness simultaneously push people between places. Amidst an increasing emphasis aimed at understanding homeless experiences in Canada, this paper provides an overview of the inter‐regional mobilities of people experiencing homelessness in Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".