Bibliographic record
Abstract
At the world level, the 20th century saw an increase from 220 million urbanites in 1900 to 2.84 billion in year 2000. The present century will match this absolute increase in about four decades. Developing regions, as a whole, will account for 93% of this growth [1]. Until now humankind has lived and worked primarily in rural areas. But the world is about to leave its rural past behind. Today we are witness, for the first time, that more than half of the globe?s population is living in towns and cities. The number and proportion of urban dwellers will continue to rise quickly. Urban population will grow to 4.9 billion by 2030. At the global level, all of future population growth will be in towns and cities [1]. Two centuries ago there was only one city on the planet that could say it had a million inhabitants - that was London. Today more than 400 cities can boast that - 408 to be precise, according to the Earth Policy Institute. But today a population of 1 million people means nothing; we are moving into the era of megacities of 10 million (and more) people. Today, there are 20 so-called megacities, whose population, and therefore energy needs, easily exceed some countries population, according to Earth Policy Institute. More people now live in Tokyo than Canada, for example [2]. Despite only occupying 2% of the world's surface area, they are responsible for 75% of the world's energy consumption.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.080 | 0.014 |
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".