Human Vulnerability, Dislocation and Resettlement: Adaptation Processes of River‐bank Erosion‐induced Displacees in Bangladesh
Bibliographic record
Abstract
The purpose of this research was to identify and analyse patterns of economic and social adaptation among river-bank erosion-induced displacees in Bangladesh. It was hypothesised that the role of social demographic and socio-economic variables in determining the coping ability and recovery of the river-bank erosion-induced displacees is quite significant. The findings of the research reveal that displacees experience substantial socio-economic impoverishment and marginalisation as a consequence of involuntary migration. This in part is a socially constructed process, reflecting inequitable access to land and other resources. Vulnerability to disasters is further heightened by a number of identifiable social and demographic factors including gender, education and age, although extreme poverty and marginalisation create complexity to isolate the relative influence of these variables. The need to integrate hazard analysis and mitigation with the broader economic and social context is discussed. It is argued that the capacity of people to respond to environmental threats is a function of not only the physical forces which affect them, but also of underlying economic and social relationships which increase human vulnerability to risk. Hazard analysis and mitigation can be more effective when it takes into account such social and demographic and socio-economic dimensions of disasters.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".