Environmental assessment after the 2004 tsunami: a case study, lessons and prospects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humanitarian aid projects carried out after the Southeast Asia tsunami must protect, conserve and manage environmental resources for sustained household and community recovery. This paper explores the role and contributions of environmental assessment (EA) in assessing and managing the impacts of these projects. The focus is on community-based EA of small, village-level rehabilitation and reconstruction projects typically implemented by nongovernmental organizations for long-term recovery. Lessons from an EA case study of housing reconstruction in Indonesia show that community EA can provide timely information for protecting water supply and reducing risk of slope movement, and that community participation can provide useful input for site planning, rehabilitating farmland and securing land title for women-headed households. These contributions are useful for sustainable project design, local resource management, and facilitating the transition from temporary to permanent housing. Future prospects for community EA include strengthening linkages among strategic and rapid forms of EA, compliance with EA requirements increasingly reinstated after the emergency phase, and greater use of supplementary or alternative EA approaches such as class assessments.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it