Bibliographic record
Abstract
Developed, modern cities throughout the world are facing population declines at an unprecedented scale. Over the last fifty years, 370 cities throughout the world with populations over 100,000 have shrunk by at least 10 % (Oswalt and Rieniets 2007). Wide swaths of the U.S., Canada, Europe, and Japan are projecting double-digit declines in population in the coming decades. Internationally, scholars and practitioners of the built environment have responded to this crisis by reconceptualizing decline as shrinkage and have begun to explore creative and innovative ways for cities to successfully shrink (Stohr 2004; Swope 2006). The lack of strong market demand and an abundance of vacant land create unprecedented opportunities to improve green space networks and natural systems in shrinking cities. Capitalizing on decline to set aside land for recreation, agriculture, green infrastructure, and other non-traditional land uses will benefit existing residents and attract future development, and enable shrinking cities to reinvent themselves as more productive, sustainable, and ecologically sound places. We offer two central challenges to the academic planning community to consider addressing in the shrinking cities phenomena. First, little is known about how existing
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".