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Record W4408090193 · doi:10.1038/s44168-025-00228-3

A systematic analysis of disability inclusion in domestic climate policies

2025· article· en· W4408090193 on OpenAlexafffund
Sébastien Jodoin, Amanda Bowie-Edwards, Katherine Lofts, Sajneet Mangat, Bianca Adjei, Alexandra Lesnikowski

Bibliographic record

Venuenpj Climate Action · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsConcordia UniversityMcGill UniversityEnvironment and Climate Change Canada
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInclusion (mineral)HarmHuman rightsPolitical scienceMedical model of disabilityAction (physics)Face (sociological concept)Climate changeState (computer science)Climate policyLaw and economicsPsychologySociologyLawSocial psychologySocial science

Abstract

fetched live from OpenAlex

We provide the first systematic analysis of whether, how, and to what extent people with disabilities and their human rights are included in two subsets of climate policies adopted by 195 parties to the Paris Agreement. We found that only 41 parties mention people with disabilities in their nationally determined contributions (NDCs), whereas only 75 do so in their adaptation policies. Moreover, these references are rarely accompanied by concrete measures to include people with disabilities, their rights, or their knowledge in climate decision-making. Our findings demonstrate that states are generally not abiding by their obligations to respect, protect, and fulfill the human rights of persons with disabilities under international and domestic law. This exposes people with disabilities to climate-related harm and reinforces, rather than disrupts, the inequities they face in societies around the world. It also fails to harness the multiple benefits associated with a disability-inclusive approach to climate action.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.193
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
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 head0.078
GPT teacher head0.402
Teacher spread0.324 · 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

Citations12
Published2025
Admission routes2
Has abstractyes

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