MétaCan
Menu
Back to cohort
Record W4412424044 · doi:10.1016/j.destud.2025.101326

Researching collaborative housing through design: A case study of co-design workshops involving low-income recent immigrants in Canada

2025· article· en· W4412424044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDesign Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsImmigrationSociologyDemographic economicsGeographyEconomicsArchaeology

Abstract

fetched live from OpenAlex

Canada's housing crisis affects low-income immigrants seeking affordable housing. One option for increasing affordability is collaborative housing. However, for collaborative housing to be adequate for immigrants, it is critical to know what designs meet their needs. This study used co-design workshops with low-income recent immigrants in Canada to explore their housing pathways, needs and preferences. Participants developed artefacts corresponding to their dream homes, to minimum acceptable housing, and to collaborative housing. These artefacts were digitally translated into five core models of collaborative housing. The results show that the Research Through Design approach, combined with co-design methods, can be used effectively to identify models of collaborative housing for low-income recent immigrants and to envision alternative housing futures.

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.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.141
GPT teacher head0.421
Teacher spread0.280 · 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