Natural Disasters and Weak Government Institutions: Creating a Vicious Cycle that Ensnares Developing Countries
Bibliographic record
Abstract
Abstract In the vast literature on natural disasters one aspect is largely unexplored, and this is the two-way relationship between natural disasters and the performance of public (government) institutions responsible for mitigating these natural disasters. The first relationship is that poor performance of public institutions responsible for mitigating natural disasters worsens the impact of natural disasters. The disaster literature is silent on the second relationship that, I argue, exists between natural disasters and public institutions: natural disasters can overwhelm the public institutions responsible for mitigating natural disasters and, as a result, it may make them even more ineffective. This paper is my attempt to fill this gap. I argue that this two-way relationship creates a particularly serious problem for developing countries, having the potential to trap developing countries in a vicious cycle: poor performance of public institutions triggering natural disasters, and natural disasters making public institutions more ineffective by overwhelming them. The exploration of this two-way relationship is necessary to have a more nuanced understanding of the ways in which natural disasters can detrimentally impact developing countries. The paper concludes that to break this vicious cycle, as a first step developing countries need to focus on institutional reform. Reform proposals should aim at improving the performance of the public institutions that are directly responsible for mitigating natural disasters. To address this challenge, scholars and governments must specifically identify the public institutions that are responsible for particular activities under review. Only then can the following questions be explored: what are the weaknesses of such public institutions, and how can their performance be improved?
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".