Disaster Financialization: Earthquakes, Cashflows and Shifting Household Economies in Nepal
Bibliographic record
Abstract
ABSTRACT The political economy literature on post‐disaster reconstruction tends to contrast ‘disaster capitalism’ narratives denouncing the predatory character of neoliberal rebuilding, and ‘building back better’ policies supporting market‐driven reconstruction. This article seeks to provide a more nuanced account, developing the concept of ‘disaster financialization’ through a case study of household‐level changes experienced through processes of post‐earthquake reconstruction in Nepal. The concept of disaster financialization describes not only the integration of disaster‐affected households into the cash‐based logic of reconstruction instituted by donors and government authorities, but also the financialization of their lives, social relations and subjectivities. It is a transitive process involving a shift into financialized mechanisms of disaster prevention, adaptation and recovery. Analysing contrasting experiences across three earthquake‐affected districts in Nepal, this study proposes disaster financialization as an integrative term through which to understand the simultaneous acceleration of monetization, the leveraging of cash incentives by donors and government to ‘build back better’, and the flurry of financial transactions associated with reconstruction processes. While some aspects of disaster financialization have had negative social impacts, such as debt‐related anxieties and a breakdown of voluntary labour exchanges hurting the most vulnerable, the process has taken on variegated forms, with equally variegated effects, reflecting household characteristics and interactions with financial institutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".