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Record W3211523461 · doi:10.9734/ejnfs/2021/v13i730434

Study of the Characteristics of Farmers on Practicing Coping Strategies towards Household Food Security during Flood Period

2021· article· en· W3211523461 on OpenAlexaff
M. E. Haque, Md. Nazrul Islam, M. Y. Uddin, M. J. Alam, Mohammad Atiqur Rahman, Muhammad Abdul Majid, Mohammad Mahfujul Haque, Md. Rafiqul Islam, Mursaleen Zebin Turin, J. Tasnim

Bibliographic record

VenueEuropean Journal of Nutrition & Food Safety · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutions123 Certification (Canada)
FundersSocial Science Research Council
KeywordsFlood mythFood securityCoping (psychology)BusinessSocioeconomicsPsychologyGeographyEconomicsAgriculture

Abstract

fetched live from OpenAlex

A study was carried out at flood affected reverine villages of three upazilas under Jamalpur district in Bangladesh during September, 2011 to May, 2012 to explore the relationship, contribution and direct–indirect effect between personal attributes and their coping strategies towards household food security practiced by the farmers during flood. Data were collected from randomly selected respondents through both the qualitative and quantitative techniques and analyzed with the help of SSPS. Out of 18 personal, economic, social and psychological characteristics of the farmers, the personal education, housing condition, annual income, annual expenditure, savings, organizational participation, participation in IGAs, cosmopoliteness, environmental awareness, knowledge on flood coping mechanisms and household food security had positive and credit received and utilization of received credit had negative while age, family size, training received, risk orientation and involvement in safety net programmes had no significant relation with coping strategies towards household food security during flood period.

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)0.000
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 score0.900
Threshold uncertainty score0.202

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 communication0.0000.000
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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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