Constraints Faced by the Farmers in Practicing Coping Strategies towards Household Food Security during Flood
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".