Strengthening resilience in ultra-poverty: using multilayered indicators approach in climate-induced vulnerability
Bibliographic record
Abstract
Bangladesh’s unique geographic location is characterized by high population density and widespread poverty. This makes it particularly susceptible to the adverse effects of climate change. This paper discusses the importance of refining poverty alleviation strategies in Bangladesh by acknowledging the diversity of poverty among different demographic groups. This study investigates the application of the multidimensional poverty index (MPI) to the ultra-poor population in Bangladesh and proposes a dual-layer approach to enhance poverty alleviation strategies. The methodology includes a comprehensive vulnerability assessment using the modified likelihood vulnerability index (MLVI) and advanced machine learning algorithms to analyze household-level data. The regression models displayed 96% accuracy. The ultra-poor population revealed only seven features that impacted them from the Gini importance algorithm. Features include ‘participation in embankment repair construction’ with the highest score of 0.198 and ‘maximum loan borrowed funds in the last five years’ with a score 0.169 as critical predictors of ultra-poverty status. The results reveal significant heterogeneity in vulnerability factors across different demographic groups, highlighting the need for tailored interventions. A new assessment tool is necessary to adapt indicators, weights, and data collection methods to local realities to address the unique challenges faced by the ultra-poor population. This results in a more comprehensive analysis than the use of the multidimensional poverty index (MPI) or the modified livelihood vulnerability index (MLVI).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".