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Record W3213088217 · doi:10.9734/arja/2021/v14i430140

Determining the Coping Strategies towards Household Food Security Practiced by the Farmers in Flood Prone Areas

2021· article· en· W3213088217 on OpenAlexaff
M. E. Haque, Md. Nazrul Islam, M. J. Alam, M. Y. Uddin, Mohammad Mahfujul Haque, Md. Rafiqul Islam, Muhammad Abdul Majid, Md Golam Mostafa, Mursaleen Zebin Turin

Bibliographic record

VenueAsian Research Journal of Agriculture · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutions123 Certification (Canada)
Fundersnot available
KeywordsLivelihoodFood securityFlood mythBusinessCoping (psychology)AgricultureSocioeconomicsMarketingGeographyPsychologyEconomics

Abstract

fetched live from OpenAlex

A study was carried out at each of three flood affected reverine villages of three upazilas (small administrative unit) under Jamalpur district in Bangladesh during September, 2011 to May, 2012 to find out the coping strategies towards household food security practices by the farmers during flood period. Data were collected from randomly selected 336 respondents of 6720 farm families through both the qualitative and quantitative techniques and analyzed with the help of SPSS. A three-point rating scale was used for measuring the coping strategies considering five components such as food preservation, food management, food collection, agricultural products protection and some social aspects. The overall situation about practicing coping strategy had medium to high level where 70.83 percent were practiced high, 29.17 percent medium and none of them were under practiced low coping strategy towards household food security. Based on the above findings, it can be said that still now there is an ample scope for the development workers to work with the flood affected people for creating awareness towards better utilization of existing resources for improving their food situation as well as livelihoods.

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 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.744

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.052
GPT teacher head0.306
Teacher spread0.254 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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