“A Community of One”: Social Support Networks and Low-income Tenants Living in Market-rental Housing
Bibliographic record
Abstract
Social networks, and the supports they provide, are often thought to be key to the survival of those living in poverty. Rooted in a partnered research initiative on the housing experiences of tenants in greatest need, we examine the social support networks of low-income renters living in market housing, while in receipt of rent subsidies and assistance from housing workers to do so. Based on 21 interviews with tenants and service providers, we find that participants in our study have very limited informal social support, which is also confined to instrumental rather than emotional dimensions. Many deliberately did not engage with those with whom they once socialized or their neighbours, and identified spatial aspects of their housing that facilitated removal from harmful networks. However, it is also clear that individuals in our study were not without ties. Despite having limited, and also actively limiting, informal ties, participants sought out and received extensive material and emotional support from non-profit organizations including harm reduction, youth and women’s centres, as well as from housing workers. Results amplify the role of these organizations beyond material survival; implications include ensuring tenants are able to access the organizations on which they rely, and adequately resourcing non-profits.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".