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Record W4415477750 · doi:10.1080/07352166.2025.2564109

Do Canadian adaptation policies address climate change impacts on the housing sector?

2025· article· en· W4415477750 on OpenAlexafffundabout
Alexandra Lesnikowski

Bibliographic record

VenueJournal of Urban Affairs · 2025
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsClimate changeClimate change adaptationAdaptation (eye)Public policyPolitical economy of climate change

Abstract

fetched live from OpenAlex

This study examines how federal, regional, and local Canadian governments address intersections between housing, climate change vulnerability, and adaptation in strategic plans. We apply a multidimensional model of policy change to characterize the current policy landscape and understand whether current efforts represent incrementalist or transformational approaches to housing adaptation. We observe that some governments are adopting more transformative language to frame the challenge of adaptation, but policy objectives and policy mixes are still strongly oriented around biophysical dimensions of climate change and rarely address social dimensions of housing vulnerability. No strategic plan advances deep policy change across all dimensions of our model, although we do observe pockets of deeper change emerging. Our findings raise important questions about the potential for transformative adaptation in the Canadian housing sector and who bears responsibility for crafting ambitious adaptation policy that addresses both biophysical and social dimensions of housing vulnerability.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.109
Threshold uncertainty score0.790

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0070.003
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.165
GPT teacher head0.356
Teacher spread0.191 · 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 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

Citations0
Published2025
Admission routes3
Has abstractyes

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