Bibliographic record
Abstract
In 1921, Canada was one of the first countries to reach the half-urban milestone. The rest of the world, combined, did not reach 50% urban until more than 85 years later in 2008. In the 1920s, global population surpassed 2 billion. One hundred years later, the population had quadrupled to more than 8 billion. For the last 100 years, the world’s attention was often on the emergence of more than 75 new countries. Geopolitical tensions were intense at times, leaving cities somewhat overlooked. Yet, they led global wealth and waste generation, which increased more than tenfold as the world urbanized. Cities, especially Canadian cities, drove this Great Acceleration largely through their ability to scale. A city that doubles in size more than doubles wealth, energy use, and waste generation. The increase is superlinear (~ 1.15). That same city that doubles in size can also do so with less than twice the infrastructure costs. Infrastructure costs increase sublinearly (~ 0.85). Countries and businesses do not benefit from this scaling superpower. As cities scale, they evolve into large urban agglomerations with complex adaptive systems that behave uncannily like natural systems. Cities can benefit by applying the contributions of two key researchers: Dana Meadows and her places to intervene in a (urban) system, and Elinor Ostrom’s the city as commons.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".