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Record W4402585764 · doi:10.5206/ijoh.2023.3.16764

Homelessness During the Pandemic: The Use of Arts to Mobilize Knowledge

2024· article· en· W4402585764 on OpenAlexvenueaboutno aff
Jean Hughes, Jeff Karabanow, Kaitrin Doll, Haorui Wu, Catherine Leviten‐Reid

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)The artsPolitical scienceEconomic growthMedicineEconomicsDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.126
GPT teacher head0.445
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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