Bibliographic record
Abstract
Japan experienced rapid urbanization after the World War II as indicated, forexample, by the fact that the population share of Densely Inhabited Districts (DID), nearly doubled between 1950 and 2000, from 34.9 percent to 65.2 per-cent, while accounting for only 3.3 percent of the national land.1 Moreover, this rapid urbanization does not appear to be a simple proportional increase of economic activities in all urban areas. Rather, the spatial distributions of indus-tries and population within the 258 metro areas (cities) of Japan are quite skewed. The population of the largest city, Tokyo, exceeded 30 million in 2000 and accounted for more than a quarter of the national population. The ten largest cities together accounted for more than a half of the national popula-tion. Moreover, if the industrial diversity of a given city is defined in terms of the number of industries exhibiting significant agglomeration within that city (see the section on cluster- based choice cities and industries below), then the population sizes of cities also appear to be highly correlated with their indus-trial diversities (see the section on the hierarchy principle).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.543 | 0.373 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".