Coping with density: reflections on Java (1960‐2010)
Bibliographic record
Abstract
The issue of uneven population density between regions of Southeast Asia was prominent in colonial literature. Colonial administrators and scholars, particularly geographers, considered that discrepancy in population density was a major obstacle to development. Among the most extreme cases of so‐called ‘overcrowded’ lands were Luzon and the Visayas, the Red River delta and the island of Java. In each of the three countries concerned, the Philippines, Vietnam and Indonesia, policies were subsequently adopted seeking to remedy the strong differences between, on the one hand, core areas with very high population densities and, on the other, much less densely populated peripheral regions. In all cases, particularly the Indonesian one, actual population deconcentration has occurred. Yet, the total population as well as average densities have continued to climb. By the early 2000s, Indonesia’s command island remained one of the most densely populated geographic realms in the world. Its density, one would be tempted to say its intensity, was also manifest in terms of agricultural land use and rates of urbanization and industrialization. This paper attempts to provide some food for thought, including reflections on the so‐called problem of high population density, particularly in the case of Java.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".