Disability in sustainability theories, models, and frameworks: a scoping review guided by the international classification of functioning and the sustainable development goals
Bibliographic record
Abstract
This review builds upon existing policy and research synergies between the World Health Organization’s (WHO) International Classification of Functioning (ICF), the Sustainable Development Goals (SDGs), and the Social Determinants of Health (SDH) Framework to identify contributions of a disability perspective to sustainability. Focusing on synthesized information from academic theories, models, and frameworks (TMFs) in peer-reviewed research, journal articles published in English and indexed in the Web of Science, Scopus, and PubMed were screened for relevance. Data charting resulted in three categories of TMF-style evidence: 1) Sustainability TMFs, 2) Interdisciplinary TMFs, and 3) Disability TMFs and other informing perspectives. A narrative summary of sustainability TMFs illustrated synergistic convergence with the Convention on the Rights of Persons with Disabilities (CRPD); as such, solutions that support the CRPD may have the potential to co-support sustainable development and vice versa. Two other categories of evidence led to a lens of TMF complexity as well as multiple connections between environmental (ecological) sustainability and disability. As guided by the SDGs, the outline of evidence resulted in two frameworks. First, a framework facilitates the mapping of key synergies such as transportation and built environment to minimize travel distance and reduce land use, climate emissions, air pollution, and travel cost, time, and risk – which in turn influence access to food, school, employment, healthcare, and community participation under the SDGs. Second, a framework is collated to consider ten guiding principles, under which the complexity of sustainability-disability TMFs can be streamlined to inform future policy and practice.
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 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 | 0.027 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".