Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it