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Record W4379055850 · doi:10.5206/ijoh.2022.2.15213

Ending Homelessness in Canada: Reflections from Researchers in the Field

2023· article· en· W4379055850 on OpenAlexafffundvenueabout
Kristy Buccieri, Nicole Whitmore, James M. Davy, Cyndi Gilmer

Bibliographic record

VenueInternational Journal on Homelessness · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsTrent University
FundersGovernment of Ontario
KeywordsRestructuringBlueprintGovernment (linguistics)Public relationsPolitical scienceIdeologyField (mathematics)Welfare reformWelfarePublic administrationSociologyEconomic growthPoliticsLawEconomics

Abstract

fetched live from OpenAlex

Ten-year plans to end homelessness have become common in communities across Canada, yet homelessness persists. This study brings together experts in the field of homelessness to gain insight into whether homelessness can be ended and what steps need to be taken to accomplish this. Twenty-six Canadian homelessness researchers participated in video-recorded structured interviews in the summer of 2021. They were asked whether, and how, homelessness could be ended in Canada. Interviews were transcribed and analyzed for recurring themes. There was widespread agreement across the participants that homelessness could be ended in Canada by focusing on four distinct yet related areas. First, all levels of government must be held accountable for policy decisions they make, and they must learn from other countries, such as Finland, where social welfare policies are more robustly integrated. Second, Canada must continue to implement policies and laws that prioritize housing as a human right rather than a commodity. Third, there is a need for individualized choice-based supports, following the principles of Housing First, which emphasize community integration. Finally, preventive measures must be prioritized with a focus on improved systems alignment and discharge planning between institutions. This paper does not offer a blueprint for change, recognizing the extent of public and social policies, tax restructuring, and ideological shifts that will need to occur. Rather, it provides a thoughtful reflection from researchers on where we as a nation should focus our attention if we want to end homelessness in Canada.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.187
GPT teacher head0.493
Teacher spread0.306 · 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.

Study designObservational
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

Citations3
Published2023
Admission routes4
Has abstractyes

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