THE ROLE OF LOW-COST HOUSING IN THE PATH FROM VULNERABILITY TO RESILIENCE
Bibliographic record
Abstract
It is well known that low-cost housing not only reflects, but also greatly influences the vulnerability of a community. This means that post-disaster housing programs can improve the living conditions of affected families or make them even more vulnerable. However, it is still unclear how different post-disaster housing strategies enhance community resilience. This article seeks to bridge the theoretical gap that exists between vulnerability and resilience theories and to clarify how post-disaster housing programs can potentially enhance community resilience. Four different housing strategies used after the 2003 earthquake in Bam, Iran, illustrate the role of housing in the path that can potentially lead communities from a vulnerable state to resilience. These strategies include: (A) Prefabricated units built on temporary camps located in the city and in the outskirts and developed by the central government, (B) Masonry units built by a public stakeholder on the yards of destroyed houses (C) Prefabricated units built by the central government in partnership with a private firm and located in the yards of destroyed houses, and (D) Hightech imported units built on the outskirts of the city. Analysing these strategies through the lens of a new framework based on a systems approach permits to identify the different impacts of post-disaster housing programs. Whereas strategies A, C and D had negative consequences in various sub-systems of the affected community, strategy B positively enhanced community resilience. The findings of the study provide insightful information that can help architects and decision makers identify the appropriate housing strategy to be implemented after disasters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".