Suburban Sprawl in the Developing World: Duplicating Past Mistakes? The Case of Kuala Lumpur, Malaysia
Bibliographic record
Abstract
Newly affluent developing world cities increasingly adopt the same unfortunate low-density suburban paradigm that shaped cities in the industrialized world. Identified by a World Bank report as a "mini-Los Angeles," Kuala Lumpur is a sentinel example of the results of unrestrained sprawl in the developing world. Factors driving sprawl included government policies favoring foreign investment, "mega-projects," and domestic automobile production; fragmented governance structures allowing federal and state government influence on local planning; increasing middle-class affluence; an oligopoly of local developers; and haphazard municipal zoning and transport planning. The city's present form contributes to Malaysia's dual burden of disease, with inner-city shantytown dwellers facing communicable disease and malnutrition while suburban citizens experience increasing chronic disease, injury, and mental health issues. Despite growing awareness in city plans targeted toward higher density development, Kuala Lumpur presents a warning to other emerging economies of the financial, societal, and population health costs imposed by quickly-built suburban sprawl.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".