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Constraints Faced by the Farmers in Practicing Coping Strategies towards Household Food Security during Flood

2021· article· en· W3209705167 on OpenAlexaff
M. E. Haque, Md. Nazrul Islam, Md. Rafiqul Islam, Mohammad Mahfujul Haque, Muhammad Abdul Majid, J. Tasnim, Mohammad Atiqur Rahman, M. J. Alam, M. Y. Uddin, K. M. Rahman

Bibliographic record

VenueSouth Asian Journal of Social Studies and Economics · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutions123 Certification (Canada)
FundersSocial Science Research Council
KeywordsFlood mythFood securityAgricultureBusinessCoping (psychology)Environmental planningFocus groupEnvironmental resource managementEconomic growthGeographyMarketingEconomicsPsychology

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 constraints faced by farmers in practicing coping strategies towards household food security during flood. The qualitative information as obtained from the focus group discussion (FGD) and scored causal diagrams (SCDs) were used to supplement the quantitative data to add new information if necessary in descriptive manner. Priority of constraints are differ from one farmer to another due to cultivated land topography, involvement of family labour in cultivation processes, precautionary measures taken against flood, previous experience, prediction of flood damage etc. ‘Lack of appropriate agricultural rehabilitation program’, ‘improper and inadequate relief distributed by GO/NGO’, ‘high price of agricultural inputs’ and ‘lack of technical knowledge on flood’ were identified as major constraints being faced by farmers in practicing flood coping strategy towards household food security. Lack of flood resistance crop varieties, inadequate organizational (GO/NGO) relief in time, lack of credit, agricultural labour and quality seeds in time (after flood) were found major constraints by almost all categories of farmers for overcoming food crisis created by the whim of nature. Go and NGO can take necessary steps against this adverse situations.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.402

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 studies0.0010.000
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 head0.030
GPT teacher head0.234
Teacher spread0.205 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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