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Record W4402640092 · doi:10.5206/ijoh.2023.3.18987

Addressing Poverty and Homelessness is Central to an Equity-Focused Response to Climate Change: Considering Canada as an Example

2024· article· en· W4402640092 on OpenAlexvenueaboutno aff
Sean A. Kidd, Mariya Bezgrebelna, Susan Chiblow, Maya Gislason, Samantha Green, Finola Hackett, Melissa Hiebert, Christina E. Hoicka, G.N.C. Kenny, Robert D. Meade, Pemma Muzumdar, Renee Rosenmann, Vicky Stergiopoulos, Denise Thomson, Pranita Bhushan Udas, Pierre Valois, Liette Vasseur, Lewis Williams, Gregor Wolbring, Haorui Wu, Edward Xie, Shelby Yamamoto

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyClimate changeEquity (law)EconomicsNatural resource economicsEnvironmental scienceDevelopment economicsPolitical scienceClimatologyEconomic growthGeologyOceanography

Abstract

fetched live from OpenAlex

People experiencing poverty are highly exposed to climatic events due to multiple intersecting factors. This commentary centres on poverty, generally, and homelessness, specifically in considering the impacts of climate change on health equity in Canada. We propose prioritizing poverty reduction and prevention over emergency response through measures such as universal basic income and enhanced housing standards. Such work can be grounded in inclusive assessments of risks and leveraged interventions, Indigenous leadership, intersectoral collaboration, and community engagement. We argue that it is essential that we move beyond the current, broad awareness of climate change as a threat multiplier with an emphasis on individual responsibility and the “othering” of people experiencing poverty. What is needed is a clear, systems-focused action fundamentally different from the pace and nature of the work done to date.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.000
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 head0.223
GPT teacher head0.402
Teacher spread0.179 · 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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal on HomelessnessSame topicClimate Change, Adaptation, MigrationFrench-language works237,207