Contribution of the Characteristics of Farmers to use of Coping Strategies towards Household Food Security
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 contribution of the characteristics of the farmers to the use of coping strategies towards household food security practiced by the farmers during flood. Data were collected from randomly selected 336 respondents both the qualitative and quantitative techniques and analyzed with the help of SPSS. Out of 18 characteristics, 11 had positive, 2 had negative and 5 had no significant relation with coping strategies towards household food security during flood period. Stepwise regression analysis revealed that six variables namely participation in income generating activities (IGAs) (20.1 percent), knowledge on flood coping mechanisms (8.5 percent), cosmopoliteness (6.5 percent), utilization percentage of received credit (2.0 percent), water and sanitation condition (1.6 percent) and year round household food situation (0.8 percent) were the major contributing variables which combindly explained 39.5 percent of total variations. Path analysis revealed that knowledge on flood coping mechanisms had the highest positive direct effects (0.285) and participation in IGAs had highest positive indirect effects (0.169) on coping strategy practices. Considering the relative contribution on the coping strategy practices towards household food security during flood period, based on their direct effects, the six variables could be arranged as follows knowledge on flood coping mechanism > participation in income generating activities > cosmopoliteness > water and sanitation condition > year round household food situation > utilization of received credit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".