Homelessness, COVID-19, and discourses of contagion
Bibliographic record
Abstract
In March 2020, when the COVID-19 pandemic began in Canada, public health and medical authorities quickly identified emergency shelters and people experiencing homelessness as particularly at risk of contracting and spreading COVID-19 (Knight et al., 2021). Drawing on interviews with 28 service providers in organizations that primarily serve people experiencing homelessness in Ottawa, Ontario, Canada and a media scan, we explored how people who worked in and accessed these organizations negotiated discourses of contagion and infection throughout the COVID-19 pandemic. This paper is informed by Goffman's (1963) theory of stigma, complemented by Crawford's (1994) idea of the Self and unhealthy Other. We argue that people experiencing homelessness, the spaces that they occupy, and the people they engage with, have been discursively marked as dangerous vectors of infection who present a risk to the health of the whole population, rather than as vulnerable to the health consequences and social disruption of COVID-19. Consequently, people experiencing homelessness have experienced further stigmatization throughout the pandemic as they have been separated from their communities, friends, and families, left without support or shelter, internalized blame for the spread of COVID-19, and faced dehumanization, grief, and trauma resulting from uneven COVID-19 interventions. We highlight these findings to support the application of trauma- and violence-informed care in service settings to prevent the further traumatization of people experiencing homelessness in services intended to support them.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.032 | 0.072 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".