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Record W4414606466 · doi:10.1111/sjtg.70025

Coping with density: reflections on Java (1960‐2010)

2025· article· en· W4414606466 on OpenAlexaff
Rodolphe De Koninck, Phạm Thanh Hải

Bibliographic record

VenueSingapore Journal of Tropical Geography · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsUrbanizationPopulationCoping (psychology)IndonesianColonialismJavaPopulation densityObstacle

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.343
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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