Advocacy for the Compact, Mixed-Use and Walkable City: Designing Smart and Climate Resilient Places
Bibliographic record
Abstract
Urban areas currently account for 60 to 80 per cent of global energy consumption, 75 per cent of carbon emissions and more than 75 per cent of the world's natural resources. A conference on the appropriate transformation of urban systems is therefore important and timely, as it is essential to deal with the future increase in urban populations, current overconsumption and cities’ growing footprints despite finite resources and limited availability of land. Therefore, it’s timely to highlight the need for taking steps to address greenhouse gas emission reductions and the global nature of the challenge. While the knowledge of good urban design allowed us for centuries to design cities that functioned well and had beautiful proportions, now an entirely new set of questions about optimal city form and urban management have emerged that have not previously been asked. In this keynote address, firstly I will outline the qualities of authentic urban places and offer a definition of ‘Smart City’; and then I will argue that urban design still warrants a very high priority of good public space for face-to-face encounters as it sets the framework for success of any future urban development at an early stage and remains central to any successful low carbon outcomes. In all this, urban form, public space, density and the integration of low-carbon technologies all have a strong interrelationship.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".