Investigating the Climate-Induced Livelihood Vulnerability Index in Coastal Areas of Bangladesh
Why this work is in the frame
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Bibliographic record
Abstract
Understanding the complex dynamics of a household’s livelihood and potential vulnerabilities in the face of climate change is challenging. This research paper considers the Shyamnagar sub-district in the southern part of Bangladesh to analyze the complex issues of the vulnerability of livelihoods in the face of climate change. We conducted a questionnaire survey (n = 156) of approximately 15.6% of households in the study area. Consequently, we collected Geographical Information System (GIS) data and satellite imagery to demonstrate the land-use changes concerning vulnerabilities. A total of 54 indicators were selected to assess the livelihood vulnerability index, considering the demographic profiles, livelihood strategies, social networks, food security, water security, income, physical infrastructures, access to health services, and impacts of natural disasters. The results of the study demonstrate that only 21% of the people in the studied regions are less vulnerable to livelihood impacts in the face of climate change, while 23% of the households remain the most vulnerable. Moreover, inadequate social networks and inefficient livelihood strategies are contributing the most to the household vulnerability indices. Interestingly, the impacts of natural disasters remain the same for the whole study area and endure similarly when assessing household vulnerability. Finally, the study reveals that decision-makers may formulate effective adaptation policies to safeguard people and their livelihoods in the time of unprecedented climatic conditions in this unique area of Bangladesh.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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 it