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Beyond the Single Story of Climate Vulnerability

2024· article· en· W4402695006 on OpenAlexaff
Sarah Bell, Sébastien Jodoin, Tanvir Bush, L.R. Crow, Siri Eriksen, Emma Geen, Mary Keogh, Rebecca Yeo

Bibliographic record

VenueInternational Journal of Disability and Social Justice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsMcGill University
Fundersnot available
KeywordsVulnerability (computing)Climate changeHistoryGeographyComputer scienceComputer securityGeologyOceanography

Abstract

fetched live from OpenAlex

Health. Disability. Vulnerability. These words are often used when discussing the risks of climate disruption. These discussions warn of the potential for climate impacts to “undermine 50 years of gains in public health” (as stated by the Lancet Countdown on Climate Change). Increasingly, such discussions also acknowledge climate injustice, examining who will benefit or lose out from climate change, how and why. The embodied vulnerability of disabled people is often assumed within such discussions, with less consideration of the social, economic or political conditions that create this vulnerability. By bringing disability justice and disability studies into correspondence with care, environmental and climate justice scholarship, this reflective paper challenges the master narratives that blur differentiated experiences of disability and climate impacts into a single story of inevitable vulnerability. Recognising disabled people as knowers, makers and agents of change, it calls for transformative climate action, underpinned by values of solidarity, mutuality and care.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.042
Scholarly communication0.0080.014
Open science0.0010.011
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.085
GPT teacher head0.372
Teacher spread0.287 · 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 designTheoretical or conceptual
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

Citations9
Published2024
Admission routes1
Has abstractyes

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Same venueInternational Journal of Disability and Social JusticeSame topicClimate Change, Adaptation, MigrationFrench-language works237,207