Path Dependence, Abnormal Times and Missed Opportunities: Case Studies of Catastrophic Natural Disasters From India and Nepal
Bibliographic record
Abstract
Abstract Path dependence literature largely accepts that large-scale disasters trigger abnormal times that weaken path dependence and create windows of opportunity to bring about institutional reforms. Disaster literature insists that lessons must be learnt from past disasters, so that damage caused by future disasters can be mitigated. Yet experience suggests that institutional reforms are rarely implemented post catastrophic disasters. This paper examines factors that might explain why the windows of opportunity triggered by disasters are missed in some cases, while seized in others. This question is explored by juxtaposing two case studies: the Gorkha 2015 earthquakes (Nepal) and the Uttarakhand 2013 flood (India), the worst natural disasters to have struck the regions. Analyzed through the insights of path dependence, the case studies reveal that post disasters institutional reforms were implemented in Nepal, aimed at improving implementation of building construction and zonal laws by public institutions. However, no such institutional reforms were implemented in India, specifically Uttarakhand. A comparative analysis identifies similarities and differences in actions taken by public institutions before and after the disasters aiming to improve public institutions’ implementation of laws, to explore factors explaining the contrasting outcomes. The paper reveals key distinctions highlighting the critical role of (a) gradual reforms taken during normal times and its influence on actions taken during abnormal times; and (b) negative feedback provided by public institutions responsible for implementing building construction and zonal laws (implementing agencies), and by other public institutions, and denial or acknowledgement of such critique by implementing agencies. Based on the findings, the paper elaborates policy suggestions that may aid in mitigating the possibility of abnormal times repeatedly becoming missed opportunities. More specifically, this paper provides a starting point for exploring what might be done during normal times so that when disasters do occur in the future, these opportunities can be seized and used to bring about reforms to improve public institutional functioning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".