On the Front Lines: Nonprofits in the Homeless-serving Sector During the COVID-19 Pandemic
Bibliographic record
Abstract
This article examines the experiences of the nonprofit, homeless-serving sector during the first wave of the COVID-19 pandemic. Qualitative interviews were conducted with staff and volunteers from frontline organizations in the two largest communities in Nova Scotia, Canada. Participants reported much strain on their organizations' human resources, but also the ability to adjust service delivery mechanisms quickly in order to continue offering supports. Most reported greater in-kind contributions from businesses and community members as well as more funding from the federal government in particular, albeit with administrative burdens and defined timelines. Nonprofits played a leadership role in developing responses to serve the needs of those experiencing homelessness, including developing comfort centres, installing portable toilets in downtown locations, and moving those without housing into hotels. They also advocated to government for state-level responses to those without housing, including calls to invest in new units and enhance funding for frontline service providers. At the same time, nonprofits reported working across sectors, noting better communication and relationships with state actors as well as other nonprofit organizations as a result of their COVID-19 response.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".