Maladaptation, fragmentation, and other secondary effects of centralized post-disaster urban planning: The case of the 2011 “cascading” disaster in Japan
Bibliographic record
Abstract
Previous studies have documented the negative impacts and unexpected secondary effects of post-disaster housing development. Here, we build on this tradition to explore how post-disaster urban planning and risk mitigation measures affect internal migrations after a major disaster. In the aftermath of the 2011 Great East Japan Earthquake and Tsunami, the Japanese government put forward significant efforts to provide safe housing and land in more than 20 cities. We used GIS spatial analysis to identify urban footprint changes, which proved to be reliable indicators of internal migration. Our results reveal the secondary effects of planning interventions, and more specifically, how the maladaptation measures triggered rapid urban sprawl and increased risks of landslides and vulnerabilities in mountainous areas. We also find increased urban fragmentation, both socially and spatially. Maladaptation, urban fragmentation, and rapid changes in urban footprints emerged as the consequences of centralized government-mandated planning and housing development. We conclude that the uncertainty surrounding dynamic recovery processes requires incremental adaptive action. Planners and local authorities must recognize and remain attentive to the cascading effects of centralized planning decisions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".