Bibliographic record
Abstract
The Accessibility for Ontarians with Disabilities Act (AODA) sets out a legal mandate to achieve accessibility for Ontarians by the year 2025. As the AODA is rolled out across Ontario, large urban centres like Toronto may experience challenges in implementing its standards, particularly as monitoring of the legislation is sporadic and there is no concrete definition for ‘full accessibility’. Consequently, urban planners, among other professionals, struggle to plan and design environments that are inclusive to users of all abilities. A content analysis of academic literature, policy, and best practices, as well as qualitative survey data among eight planning and planning related professionals has helped to address the following research questions: Is Toronto on track to becoming a fully accessible city by the year 2025, as set out in the Accessibility for Ontarians with Disabilities Act (2005)? And are planners adequately trained to design accessible built environments, beyond minimum provincial and municipal standards? Key words: An article on accessibility planning in theory and practice, used the key words: accessibility; built environments; urban planning; urban design; universal design; inclusive design; profession.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".