âDesign for the old and you include the youngâ - revitalising urban design in regional Australia
Bibliographic record
Abstract
The eminent Professor of Geriatric Medicine, Bernard Isaacs (cited in Giles-Corti et al 2008) said, Design for the young and you exclude the old; design for the old and you include the young. This would appear to be a useful design principle for all public spaces, but especially those with the restorative qualities found in even modest, naturalistic settings in urban areas (Kaplan and Kaplan 2011). A restorative network of walkable routes and destinations in regional towns could be influential in creating a sense of well-being and improve their attractiveness to the 65 years plus demographic set to become nearly a quarter of the population in coming decades.\nWalking, as a human activity, is linked to the evolution of our senses and should not be regarded as just a movement mode (Nicholson 2008). Walking has, in fact, helped to mould our societies and cultures. It has played a significant role in the fields of philosophy, spirituality, sexuality, literature, history, science, politics, the design of cities and many other fields (Gros 2009).\nThis paper examines the need for regional and local councils to reorient their urban design and planning policies and practices to focus on the many benefits of walking. There is more to walking than active transport and more to active transport than cycling. Or as Jan Gehl observed: We have a department for roads, why not a department for pedestrians? (Bennett 2015).
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".