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Record W4361003169 · doi:10.1080/02722011.2023.2170155

Mapping Homelessness Research in Canada

2023· article· en· W4361003169 on OpenAlexafffundabout
Alison K. Smith, Anna Kopec

Bibliographic record

VenueThe American Review of Canadian Studies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCarleton UniversityUniversity of Toronto
FundersMitacsUniversity of Toronto
KeywordsSociologyPoliticsQualitative researchPerspective (graphical)Social researchCriminologyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

What is known about homelessness in Canada? In this article, we present the results of a systematic literature review of peer-reviewed research produced on homelessness in Canada, in English and French, since 2000. We seek to map this literature in an effort at understanding how homelessness has been studied by researchers and to identify potential gaps in this impressive body of literature. The literature review included a two-stage process. First, we analyzed almost 1000 articles specifically regarding homelessness according to title, journal, and case. Then, we conducted a qualitative abstract analysis of 251 papers written by the ten most prolific scholars of homelessness research, analyzing the research question, methods, and recommendations. We find that the majority of research on homelessness in Canada has been in large cities (Vancouver, Toronto, and Montreal). Research is often conducted in comparative perspective, though there have been fewer international comparisons, and often from a public health or medical science perspective. We argue that social scientists have a lot to contribute to this field of study by analyzing the structural and political causes of homelessness, and that researchers should study small, mid-sized, northern, and rural communities in their studies as well as big cities.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.007
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.281
GPT teacher head0.515
Teacher spread0.234 · 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 designQualitative
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

Citations7
Published2023
Admission routes3
Has abstractyes

Explore more

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