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Record W3161125498 · doi:10.1080/08882746.2021.1922044

Pandemic precarity and everyday disparity: gendered housing needs in North America

2021· article· en· W3161125498 on OpenAlexafffundabout
Brenda Parker, Catherine Leviten‐Reid

Bibliographic record

VenueHousing and Society · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCape Breton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrecarityPandemicCoronavirus disease 2019 (COVID-19)Political scienceSociologyGender studiesGeographyEconomic growthEconomics

Abstract

fetched live from OpenAlex

We identify key issues for housing researchers, practitioners, and advocates working in the United States and Canada to consider, both during the COVID-19 pandemic and far beyond. First, we draw upon feminist and intersectional literatures on gendered inequalities and social structures, which provide the often forgotten or overlooked context for women’s experiences in housing. This includes the broader insight that too frequently, women have not been involved in shaping the policy and planning climate around housing, even as they disproportionately are affected by them. Second, we describe women’s housing-related precarity and some of its implications, grounding this research in a political economic critique of the way that housing and resources are allocated and the neoliberal climate that values profit over people and that has induced instability for many women in so many communities. We conclude by offering examples of organizations and initiatives that work to address the disparities identified herein. Throughout the paper, we emphasize the need for intersectional and interdisciplinary collaborations (for example, among queer, anti-racist, feminist, political economic, and other scholars) that engage with complexity and orient toward equity and justice.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.366
Teacher spread0.304 · 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 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

Citations34
Published2021
Admission routes3
Has abstractyes

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