Investigating Loss and Damage in Coastal Region of Bangladesh from Migration as Adaptation Perspective: A Qualitative Study from Khulna and Satkhira District
Bibliographic record
Abstract
This study aims to examine the loss and damage experienced by coastal regions from the perspective of adaptation. It also seeks to evaluate the adaptation techniques employed when migration is utilized as a significant approach to mitigate the effects of loss and damage on coastal communities. This study evaluates the extent of loss and damage caused by constraints on adaptation. Two districts, Khulna and Satkhira, in the Khulna division of Bangladesh, were chosen for the study. In these districts, a total of twenty-four detailed interviews and one focus group discussion (FGD) were conducted with individuals living in rural areas whom climate-related effects and disasters have impacted. Additionally, seven interviews were conducted with climate migrants residing in informal settlements within the words of Khulna City Corporation. The process of identifying appropriate interview candidates involves utilizing a combination of specific criteria and snowball sampling techniques. The study employed NVivo 14 software to conduct theme analysis on textual data obtained from interviews. In the coding procedure, we sequentially employed semantic coding, latent coding, categorization, pattern exploration, and theme creation, all of which were in line with the research aim. The study indicates that most affected persons utilize seasonal and temporary movement as an adaptive strategy to deal with the slow effects of climate change, such as increasing temperatures and salinity in rural regions, and when they encounter limitations in their ability to adapt. Conversely, they opted for permanent migration in response to stringent constraints imposed by severe climate events like cyclones and river erosion, leaving them with no alternative but to move to urban regions. Social networks are crucial in influencing migration choices, as several families depend on information provided by urban relatives and rural neighbors to inform their relocation decisions. Nevertheless, not all individuals impacted by the situation express a desire to relocate; others opt to remain in rural areas due to their sentimental attachment to their birthplaces and a sense of dedication to their ancestral territory. Due to the exorbitant cost of urban life, they believe that opting not to migrate is a more practical option for addressing the repercussions of climate-induced loss and damage. The study’s findings aid policymakers in determining migration strategies and policies to address the adverse effects of coastal population displacement in Bangladesh. Additionally, it aids in determining strategies to address the challenges faced by climate migrants in both urban and rural environments.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 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".