From fatalism to resilience: reducing disaster impacts through systematic investments
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
This paper describes a method for reducing the economic risks associated with predictable natural hazards by enhancing the resilience of national infrastructure systems. The three-step generalised framework is described along with examples. Step one establishes economic baseline growth without the disaster impact. Step two characterises economic growth constrained by a disaster. Step three assesses the economy's resilience to the disaster event when it is buffered by alternative resiliency investments. The successful outcome of step three is a disaster-resistant core of infrastructure systems and social capacity more able to maintain the national economy and development post disaster. In addition, the paper considers ways to achieve this goal in data-limited environments. The method provides a methodology to address this challenge via the integration of physical and social data of different spatial scales into macroeconomic models. This supports the disaster risk reduction objectives of governments, donor agencies, and the United Nations International Strategy for Disaster Reduction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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