Homelessness During the Pandemic: The Use of Arts to Mobilize Knowledge
Bibliographic record
Abstract
The following is a review of three arts-based research projects developed to mobilize knowledge in understandable ways for diverse populations regarding the stories of people who are homeless. All three projects were carried out in Canada – each with a different focus. The first research project created a film – Walking Through Wonderland (2010) - that captured the daily lived experiences of many street youths, such as being unable to find places to sleep, as well as not having food, or access to health care and social services, particularly for mental health care. Additionally, the film highlighted that many youths had become homeless because of major family trauma. The second research project created a Book of Images (2018) that visually portrayed the stories regarding housing stability among young people – in Halifax, Nova Scotia, and Toronto, Ontario – who had previously experienced homelessness. The third research project created an arts-based film production, Homelessness During the Pandemic (2022), with visual images and classical, almost haunting, music for the background to illustrate the experiences of homelessness during the coronavirus disease of 2019 (COVID-19) in two communities of Nova Scotia –Cape Breton Regional Municipality (CBRM) and Halifax Regional Municipality (HRM).
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".