Canadian homeless mobilities: relational perspectives on At Home/Chez Soi participants’ interurban migrations
Bibliographic record
Abstract
This thesis examines the mobility patterns of 613 participants from the At Home/Chez Soi Research Demonstration Project on Mental Health and Homelessness who were surveyed in five Canadian cities (Vancouver, Winnipeg, Toronto, Montréal, and Moncton). Participants’ mobility histories are treated as life courses: visualized using a GIS spatiotemporal analysis and complemented by examining their self-described reasons for movement (n=1,750). I contend that homeless mobilities are complex, entangled, and multiple. To better understand these mobilities, I apply relational theoretical perspectives to literature from the mobilities turn. I conceptualize mobility as composed of the relations between various actors. These relations coordinate amidst social differences, histories, and orderings of power. Together, actors and the relations between them, become more than the sum of their parts. To see mobility relationally, is to say that mobilities have emergent properties that reproduce, deepen, or ameliorate marginalization for those experiencing homelessness. I identify a series of actors and their relations composing homeless mobilities via time-space mapping, descriptive statistics, and the exploratory coding of survey data. I conclude by detailing a relational view of homeless mobilities while suggesting that expulsion is one emergent property of this system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".