Advancing disability-inclusive climate research and action, climate justice, and climate-resilient development
Bibliographic record
Abstract
Globally, more than 1 billion people with disabilities are disproportionately and differentially at risk from the climate crisis. Yet there is a notable absence of climate policy, programming, and research at the intersection of disability and climate change. Advancing climate justice urgently requires accelerated disability-inclusive climate action. We present pivotal research recommendations and guidance to advance disability-inclusive climate research and responses identified by a global interdisciplinary group of experts in disability, climate change, sustainable development, public health, environmental justice, humanitarianism, gender, Indigeneity, mental health, law, and planetary health. Climate-resilient development is a framework for enabling universal sustainable development. Advancing inclusive climate-resilient development requires a disability human rights approach that deepens understanding of how societal choices and actions-characterised by meaningful participation, inclusion, knowledge diversity in decision making, and co-design by and with people with disabilities and their representative organisations-build collective climate resilience benefiting disability communities and society at large while advancing planetary health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.004 | 0.044 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".