A systems view of temporary housing projects in post‐disaster reconstruction
Bibliographic record
Abstract
Natural cataclysms (earthquakes, hurricanes and so forth) become natural disasters when they coincide with vulnerabilities; unfortunately, informal settlements in developing countries are only too often highly vulnerable – a reality amply and unhappily confirmed by available statistics. In this context, reconstruction projects are sandwiched between the short‐term necessity to act promptly and the long‐term requirements of sustainable community development – a situation that is currently reflected in alternative and conflicting paradigms at the policy level. Adopting a case‐study approach, we explore the use of temporary housing within two post‐disaster environments, where the impact of different organizational designs leads to fundamentally different solutions to the short‐term housing problem. Our research adopts a dynamic systems approach, associating strategic organizational team design with the development of tactical technical proposals. Two case studies from Turkey and Colombia show that a coherent approach to the sequential stages of providing immediate shelter, temporary housing and permanent reconstruction is not always obtained. The research results emphasize that the performance of reconstruction projects is directly linked to the design and management of the project team.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".