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Record W7113189848

Bridging the gap: Professional perspectives on cohousing and the institutions of housing production in Canada

2025· dissertation· W7113189848 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Language
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsnot available
Fundersnot available
KeywordsBridging (networking)Production (economics)Knowledge productionMode of production
DOInot available

Abstract

fetched live from OpenAlex

This research investigates how cohousing is understood, framed, and operationalized by professionals working in Canada’s planning, housing, and development sectors. While the cohousing model is increasingly recognized for its potential to address affordability, social isolation, and environmental sustainability, it remains marginal within Canadian housing systems. Drawing on semi-structured interviews with planners, developers, architects, and consultants, this study examines the institutional barriers, interpretive practices, and professional strategies that shape the implementation of cohousing. The findings highlight a core tension: although cohousing often aligns with official policy goals, such as densification, affordability, and inclusion, it does not fit easily within prevailing regulatory and financial structures. The research emphasizes the interpretive and procedural work done by professionals to navigate this misalignment, revealing the extent to which cohousing depends on informal adaptations and individual persistence. While the study does not attempt to provide a policy blueprint, it identifies key leverage points for institutional reform, including zoning, access to credit, and the professionalization of support infrastructure.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.204
Threshold uncertainty score0.923

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0500.024
Scholarly communication0.0160.004
Open science0.0030.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0120.000

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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractno

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