Bibliographic record
Abstract
Innovation is intimately linked with cities. This occurs for many reasons, many of which are entirely obvious. People congregate in cities, and through experiencing problems and sharing ideas for how to solve them, innovation comes about. There are suppliers and experts and scholars and materials and tinkerers in cities in abundance, so questions can be formulated and answered. There is capital in cities, to provide investment to support good ideas that solve problems which people are willing to pay for. There are people in cities looking for better opportunities, and who hire on when new companies scale up.But the city itself is also a topic for innovation. These are the fields of urban design, city planning, real estate development, construction, and architecture, which come together to shape the human-created environment in which now more than 3.5 billion people live. Demographers estimate that by 2100, the total aggregate urban population will be around 7.5 billion, meaning that over the next 85 years, humanity will construct new urban settings for 4 billion people. This will certainly constitute the most massive building boom in history.But what will these cities look like? How will they feel? What will it be like to live in them? Will they be squalid and polluted? Will they be the playground of cars and the bane of pedestrians? Will they be healthy and thriving, or sick and tired? Here are some interesting facts about today's cities, which tell us a lot about what we may want and not want for tomorrow's.People who have longer commute times today are less happy than people with shorter commute times. It can take up to an hour for people to recover the ability to concentrate following a long commute, and psychologists have a word for this. They call it amnesia, which occurs as people simply shut out stimulus and try to forget about their long drive as soon as it is over. The longer the commute, the more likely people are to report chronic pain, high cholesterol, and people with commutes longer than 90 minutes are the most likely to be anxious, tired, and obese.Public health experts have invented a word to describe low density suburban neighborhoods where one is obliged to drive everywhere due to the dispersion of housing and shopping and the massive highways that separate everything from everything else: obesogenic. That is, suburbia literally makes people fat, because they spend too much time driving, and not enough time walking. Imagine designing a city that is intended to make people overweight! But this is precisely what we have done ...Consequently, merely living in a sprawling, suburb-oriented community has the effect of reducing one's expected life span by four years, largely as a consequence of the diseases associated with obesity and the stress of the commuter lifestyle. In contrast with the negative impacts of disbursed suburbia, some cities have combined dense urban living arrangements in configurations that are quite pleasant, and to which people who have choice are inevitably drawn. Vancouver, Canada, is one of those cities, and the real estate development profession has created a new word to describe it: Vancouverism. Vancouver is relentlessly dense and yet manages to be charming, beautiful, and humane. It helps that the setting is so spectacular, but even without the water and the mountains, the lessons of thoughtful zoning and planning are applicable anywhere. Developers are copying Vancouver wherever they can.When thinking about city street, scholars have identified what is now referred to as the law of social geometry, which tells us about how far from the street a good porch should be. It must be close enough to allow the porch sitter to interact with the passing pedestrian, but far enough that they are not obliged to interact. Stunningly, this can be quantified. The perfect distance for urban street conviviality is 10.6 feet.However, if the cars take over a neighborhood, the results can be disastrous for the human and humane scale. …
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".