Age-Friendliness of the Urban Design Guidelines of the Cities of Kitchener and Waterloo
Bibliographic record
Abstract
The fastest growing age group in Canada is seniors aged 65 years or older (Statistics Canada’s 2006 & 2007). The population of seniors is projected to increase to 6.7 million by 2021 and 9.2 million by 2041 (nearly one in every four Canadians) (Social Development Canada, 2006a; Statistics Canada, 2007b). Similarly, Population Estimates, Waterloo Region and Ontario, 2011 and Population Projections, Waterloo Region and Ontario, 2016, 2026 & 2036 (Region of Waterloo, 2012b, 2012c) indicate that the Region of Waterloo expects an increase in its senior population by 145.4% from year 2011 to 2036. Due to increased longevity and an increased percentage of older adults, this demographic shift poses challenges for communities, including increased healthcare costs and social isolation among seniors, which may threaten their active participation in the community. \nThe research question ‘Do urban design guidelines of the Cities of Kitchener and Waterloo address the needs of an ageing population?’ motivates this study to examine the Urban Design Manuals of the Cities of Waterloo and Kitchener to determine the age-friendliness of the current urban design guidelines, and the role of the built environment in active ageing. The current urban design guidelines of the Cities of Kitchener and Waterloo are compared with the Design of Public Spaces Standards (Accessibility Standards for the Built Environment) by Accessibility for Ontarians with Disabilities Act, 2005 (AODA); the Universal Design Principles; key findings based on the literature review (Levine, 2003; Story, Mueller, & Mace, 1998); and analysed with in-depth knowledge gained through semi-structured interviews with seniors, planners, and focus groups. The participation of the seniors provided information on the gaps between what already exists and what is required. \nThe key finding of the report is that the urban design guidelines of the Cities of Waterloo and Kitchener are fairly comprehensive in addressing the needs of seniors, but there is inadequate implementation of these guidelines.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".