Liveable urban forms: planning, self-organisation, and a third way (Isobenefit urbanism)
Bibliographic record
Abstract
Urban development combines the forces of dispersal and agglomeration, often facilitated by free market forces, and this results in different patterns and self-organised ways, with both positive and negative outputs. Globally, over 6 billion people will live in cities by 2050, and this would require at least an additional 1.2 million km2 land to be built on. This huge expansion of the urban population and area requires construction at scale that avoids current urban problems such as urban heat island effects, carbon emissions, pollution, congestion, urban sprawl and excessive hard surfacing, while maintaining the physical and mental quality of life. Two basic approaches would be to let market forces freely shape our new urban areas or to impose a strong planning framework. This paper introduces a third way, Isobenefit urbanism that takes advantage of the two basic approaches to urban development. Isobenefit urbanism is a relatively recent urban development approach to shaping urban form, through an examination of centralities and localisation by a code whose implementation results in Isobenefit cities where one can walk to reach the closest centrality (where theatres, restaurant, schools, offices, promenades, shops…are located) and the closest access to green land regardless where one lives, and regardless the size of the city.
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 | 0.002 | 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 | 0.002 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".