Bibliographic record
Abstract
Cities have emerged in different parts of the world in different periods of human history and the different trajectories of urban development have been influenced by culture, geography and world trade. Broadly speaking, we can distinguish a different pattern of urban development in the USA compared to Europe while countries such as Canada and Australia show a mix of European and American influences. Historically, the ‘western’ city has taken a different form to cities in Asia, Africa and some parts of Latin America. However, economic globalisation has begun to blur the distinctions between western and non-western cities and a similar trajectory of urbanisation has emerged within a host of underdeveloped countries. Urbanisation has proceeded at a faster rate in the developing world and it poses greater environmental and social challenges within the developing countries. At the same time, we have seen the emergence of a number of global megacities that interact more directly with each other than at any other time in human history. This chapter will trace different trajectories of urban development in different parts of the world in order to show that the ‘urban challenge’ takes different forms. At the same time, many of the environmental and social challenges posed by urban ‘sprawl’ are common and the chapter aims to demonstrate a universal need for much stronger city-wide planning and governance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".