Examining the link between marginality and differential climate resilience among disaster-affected communities in southwestern Bangladesh
Bibliographic record
Abstract
This article uses a case study of one of Bangladesh’s most disaster-prone subdistricts to examine the role of marginality in determining differential climate resilience. It used a quantitative research design and a household questionnaire survey to acquire data. In order to determine the contributing factors and quantify the magnitude of the influence, ordinal logistic regression is utilized in conjunction with principal component analysis (PCA). The AHP-based indexing approach was used to quantify the degree of resilience and marginality. Results revealed a complex link between marginality and resilience in disaster-affected areas of Southwest Bangladesh. They exhibit four distinct connections, which is impressive because it demonstrates how resilient marginalized households can be and how the opposite is true. It also identifies a lack of access to the formal institutional network and support, restricted access to social support networks, exclusion from housing and public services, restricted freedom of choice networks, and lack of access to financial assets) that have a significant impact on differential resilience. Local governments or policymakers can implement several recommendations by emphasizing the factors that affect various levels of resilience, such as boosting institutional and monetary support, fortifying social networks, improving essential services, and creating livelihood opportunities.
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 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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".