Operationalizing accessibility in environmental sustainability efforts: Challenges, barriers, and opportunities
Bibliographic record
Abstract
There is growing recognition of the need to move towards climate justice in response to the climate crisis; that is, ensuring mitigation and adaptation responses centre equity, and promote the inclusion of marginalized or otherwise ‘equity-deserving’ groups, including people with disabilities. Despite this recognition, there is little empirical research exploring the intersection of disability in sustainable developments, and even less addressing the practical challenges and opportunities to operationalize a sustainability-accessibility mindset within existing organizations. Drawing from a systems perspective and the human rights model of disability as well as an empirical case study, this paper explores practical challenges and considerations of integrating accessibility into environmental sustainability projects through a critical reflection of our own experiences implementing a tactile and visual information system for multi-stream waste disposal units in public spaces. This article presents an illustrative example of the challenges and barriers of bureaucracy, corporate structures, and the shift of mental models that need to be considered in the implementation of promoting the inclusion of visually impaired individuals. We argue for an intersectional approach to environmental sustainability that addresses these challenges and barriers, and that is compatible with the disability rights motto, “Nothing about us without us” and the need for inclusive design for collaborative impact.
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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 | 0.063 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".