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Record W2116156924 · doi:10.1186/2212-9790-12-7

Negotiating risk and poverty in mangrove fishing communities of the Bangladesh Sundarbans

2013· article· en· W2116156924 on OpenAlex
Mohammad Mahmudul Islam, Ratana Chuenpagdee

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

VenueMAST. Maritime studies/Maritime studies · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsMemorial University of Newfoundland
FundersUniversität Bremen
KeywordsPovertyFishingScale (ratio)NegotiationBusinessPoverty trapEndowmentDevelopment economicsFisheryEconomic growthEconomicsNatural resource economicsGeographyPolitical science

Abstract

fetched live from OpenAlex

Small-scale fishers in Bangladesh face substantial risks due to their occupation and their geographical setting. Without any effective buffer against crises, recurring shocks and on-going risk exposure are major factors pushing fishers into poverty. Not all fishers experience these events in the same way, however, with some of them showing higher capacity to negotiate risks. In this study, we ask how fishers cope with shock, what factors differentiate them in their risk negotiations, and what implications these factors may have on poverty alleviation policy. On the basis of the study’s findings, we posit that poverty alleviation in small-scale fishing communities in Bangladesh requires interventions that target not only risk minimization, but also the endowment of fishers with socio-economic capitals to help them handle varying degrees of risk and shocks. Such policies as, for instance, providing employment for fisherwomen or providing a basic social safety net will increase the overall resilience and well-being of fisher communities.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.020
GPT teacher head0.229
Teacher spread0.210 · 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