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Record W2177233909 · doi:10.5539/ass.v11n28p109

Shrimp Cultivation and Coastal Livelihood: A focus on Bangladesh Coastal Vulnerability

2015· article· en· W2177233909 on OpenAlexvenueno aff
Sohela Mustari, Abdul Karim, Md. Shahidul Islam Sarker, Sohel Rana

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodShrimpVulnerability (computing)FishingShrimp farmingFisheryFocus groupAgricultureWork (physics)GeographyNatural disasterSocioeconomicsBusinessEnvironmental resource managementFish <Actinopterygii>AquacultureBiologyMarketingSociologyEconomicsEngineering

Abstract

fetched live from OpenAlex

Shrimp cultivation has spread out rapidly in coastal Bangladesh. Secondary literatures provides the evidence that to afford an alternative way of livelihood for the locals, shrimp cultivation extended widely throughout the coastal areas of Bangladesh. But, the extension of shrimp farming is unable to create work opportunity for the locals in true sense. In this research, by using a household survey, to know their basic demographic information, it is revealed that though 80% of the household heads main occupation is fishing and related works but only 0.71% of their main source of fishing is shrimp farm. Moreover, in-depth interviews and Focus Group Discussions disclose that shrimp cultivation of this village is controlled and retained by the rich and outside investors, not by the poor locals. This research discovered that man-made disasters, along with the natural disasters are equally responsible in generating vulnerability from shrimp cultivation.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.319
Teacher spread0.289 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2015
Admission routes1
Has abstractyes

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