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Record W4210403750 · doi:10.1080/02723638.2021.2014668

“Homes not shelters”: co-productions of home in financialized social housing for women in Vancouver, Canada

2022· article· en· W4210403750 on OpenAlexafffundabout
Samantha Thompson

Bibliographic record

VenueUrban Geography · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFinancializationValue (mathematics)SociologyPublic housingCommodityPower (physics)CommodificationAffordable housingEconomic growthBusinessEconomicsEconomyFinance

Abstract

fetched live from OpenAlex

The financialization of housing, which privileges the exchange value of housing as a commodity over its use value, disproportionately impacts low-income households, women, and people of colour by exacerbating precarious, insecure, and unsafe housing conditions, thereby shaping their experiences of home. In this paper, I engage critical geographies of home and draw on ethnographic work in low-income housing sites for women in Vancouver, Canada. I conceptualize everyday impacts of housing financialization and power relations on co-productions of home by tenants and staff. I argue that a ‘co-production of home’ through processes of home-making and un-making by different housing actors demonstrate that low-income tenants’ experiences of housing and home are impacted by intersecting social hierarchies of power, as well as the financialization of housing. Further, the impacts of housing financialization are resisted through actions like organizational policies from housing providers and tenants’ relationships with their neighbours that produce feelings of community.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: yes
Qualitativelow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: yes · About a Canadian topic: yes
Theoretical or conceptualhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.204
Teacher spread0.188 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Theoretical or conceptual
Domainnot available
GenreEmpirical · Other

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

Citations9
Published2022
Admission routes3
Has abstractyes

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