Understanding accessibility and disability in the planning profession: an examination of planners’ knowledge and practices
Bibliographic record
Abstract
Disability discrimination has been prohibited in Canada for decades, yet people with disabilities (PwD) continue to experience inaccessible built environments. Canada’s most populous province - Ontario - has well-developed accessibility legislation, however was recently evaluated to be in a “crisis state” after 20 years of implementation. Planners have profound influence on built environments, thus we asked planners about their attitudes, perceptions and knowledge of accessibility and disability through interviews. We found limited understanding of accessibility and disability (in terms of definitions, legislation, and policy), limited experience engaging with PwD in their work/workplaces, and limited educational training. Importantly - planners were eager to learn more to enhance their practice. Offering insight into the status of the profession, we conclude with six actions to help planning bodies and practitioners advance more just communities.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".