Exploring Vulnerability–Resilience–Livelihood Nexus in the Face of Climate Change: A Multi-Criteria Analysis for Mongla, Bangladesh
Bibliographic record
Abstract
This article illustrates the critical findings of an empirical investigation of resilience, vulnerability, and livelihood nexus in one of the worst cyclone-affected sub-districts “Mongla” in Bangladesh. Results obtained from the survey conducted in 2018 and 2019 explore the co-existence of climate change vulnerability and resilience at the rural household level. Additionally, the study identifies the role of assets (e.g., land, cash, and livestock) in order to enhance the resilience of poor inhabitants. Quantitative data have been collected using structured and semi-structured interviews. The outcome of the study demonstrates that the relationships between vulnerability and resilience are very complex and exist in the study area. An exciting outcome has revealed that in some places, more vulnerable people exhibit higher resilience capacity and vice versa. Furthermore, this research emphasizes that local livelihood systems may be improved if appropriate policies are considered by local government organizations in collaboration with multiple stakeholders. Consequently, the local citizens have to play their critical role to assist government policies in order to enhance resilience at the community level. Moreover, local residents can have a better understanding of their livelihood issues in the face of climate change.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".