Household recovery from disaster: insights from Vietnam’s fish kill
Bibliographic record
Abstract
In April 2016, toxic chemicals leaked into the ocean in central Vietnam during a trial of a waste discharge system for a newly built steel plant. This resulted in a significant fish kill that impacted coastal livelihoods and the seafood sector across four provinces. We surveyed 520 households to understand how people experienced this environmental disaster, and their recovery strategies. On average people stopped all fishing-related activities for over nine months: this was a period of precarity for most households. Fish farming households suffered the greatest financial losses. Fishing households, while having a lower income, recovered more quickly than fish farming households since the mobility of boats and fishing grounds afforded flexibility and adaptability. In the longer term, relatively significant financial compensation from the company responsible for the spill made a difference to household recovery and their perceptions of the disaster. We argue that this toxic spill was a major stressor for coastal households in central Vietnam, and contribute to the precarity and the livelihood resilience literatures by offering a multi-dimensional perspective to understanding household recovery strategies. This study also draws attention to the importance of better understanding financial compensation as an aspect of recovery from human-induced disasters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".