A Turning Point? Responses to COVID-19 Within the Homelessness Industrial Complex
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic affected the homeless serving sector in significant ways, including impacts on service users and service providers. In this qualitative case study from Ottawa, Canada, we conducted 28 semi-structured interviews with service providers and key informants from the homeless serving sector to learn more about responses to the pandemic. We argue that, as it is currently designed, the homeless serving sector had limited opportunity to respond to the combined crises in housing, overdose, and COVID-19 with a transformative agenda. The article draws on Dej’s (2020) concept of the homelessness industrial complex, which argues that current systems manage and sustain rather than end homelessness. And we use Lipsky’s (1969; 1980) notion of street-level bureaucracy to explore the role of service providers as they translated shifting public health guidelines into action on the ground within contexts of ambiguity and constraint. Service providers were tasked with keeping people safe from COVID-19 while managing broader social issues such as homelessness, food insecurity, mental health challenges, and an increasingly poisoned illicit drug supply. They described challenges such as narrowly directed funding and short-term and temporary solutions to homelessness. Staff faced significant occupational stress and burnout within demoralizing contexts (Kerman & Kidd, 2021; Kerman et al., 2022). Despite new partnerships and innovative approaches that emerged, responses to the pandemic in Ottawa were shaped by the homelessness industrial complex and did not significantly contribute to ending homelessness. Even so, public health measures were able to disrupt business as usual in the sector, sparking the question: what might be possible if homelessness comes to be understood as a public health crisis?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".