Social infrastructure: a way to see hidden homelessness in rural and Northern Ontario towns
Bibliographic record
Abstract
What is social infrastructure? How is it defined, if at all? This paper importantly brings together cross-disciplinary discussions that are often about social infrastructure. Examining social infrastructure in rural and northern Ontario to draw connections between reports of hidden homelessness, discussions about or in reference to social infrastructure, and the realities faced by rural and northern Ontario municipalities and communities. Using a review and analysis of these different streams of research, social infrastructure spaces are mapped according to different definitions or understandings in the disciplines. This mapping exercise is applied to Sault Ste. Marie to illustrate the complexities of addressing social issues as they unfold in small northern Ontario communities. The research suggests that a broader understanding of what social infrastructure is can help us to see spaces that are often overlooked and underappreciated by planners, but perhaps valued by individuals experiencing hidden homelessness for the sociality and comforts that they can provide. This suggests that in addressing homelessness, poverty, and economic revival, planners in rural, small, and northern communities ought to think critically about the ways in which planning policies, programs and strategies might overlook spaces use by those most marginalized. Key words: social infrastructure, hidden homelessness, rural and northern Ontario, Sault Ste.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".