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Record W6906551753 · doi:10.17605/osf.io/jz3u7

Climate change and disability : narrative report

2024· article· en· W6906551753 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsClimate changeCivil societyPopulationNarrativePolitical economy of climate changePoverty

Abstract

fetched live from OpenAlex

Climate change is a major contemporary transnational health issue. Climate change disproportionately affects vulnerable and/or marginalized population groups, such as people with disabilities. Globally, 16% of the world's population, or 1.3 billion people, live with a disability (motor, sensory, intellectual or mental). Although they constitute one of the world's largest minorities, little is known about the international civil society organizations that address the issues of the climate crisis with disabled people. Where are these organizations located? What services/information do they provide for people with disabilities? Are their services adapted? These are just some of the questions that can be answered by a targeted international mapping of civil society organizations in Canada, Subsaharan Africa and South Asia.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0230.005

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.

Opus teacher head0.043
GPT teacher head0.348
Teacher spread0.305 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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