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Human Vulnerability, Dislocation and Resettlement: Adaptation Processes of River‐bank Erosion‐induced Displacees in Bangladesh

2004· article· en· W2051069824 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDisasters · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicHydropower, Displacement, Environmental Impact
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsVulnerability (computing)PovertySocial vulnerabilityCoping (psychology)HazardContext (archaeology)Bank erosionAdaptive capacitySocioeconomic statusGeographyEconomic growthSocioeconomicsDevelopment economicsBusinessSociologyPsychologyErosionEconomicsPopulationClimate changeSocial psychologyEnvironmental healthPsychological resilienceComputer securityEcologyMedicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.389
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it