Remittance-Receiver Households' Behaviour on Agricultural Productivity in the Rural Economy
Bibliographic record
Abstract
Remittances in the rural economy are considered a lifeline for remittance-receiving households, as they spend their money within the local economy, helping accelerate domestic money flow and maximize social welfare. In this research, the authors surveyed 200 households across four sub-districts using a snowball sampling method. The main objective is to examine the effect of remittance-receiving households’ behaviour on agro-output maximization. In Model 01, for one-season paddy cultivation, AFS, HNP, AGH, ESH, and RHI show positive and statistically significant relationships with paddy production, indicating a favourable impact on agricultural productivity and local economic development. Conversely, HSZ demonstrates a negative and statistically significant relationship with paddy cultivation. In Model 02, which focuses on fish cultivation, AFS, HMF, AGH, ESH, and RHI also show positive and significant effects on fish productivity, while micro-credit plays a significant role in supporting fish production. Overall, remittance-receiving households’ behaviour influences agricultural productivity in the rural economy. When a family receives remittances, the behaviour of the household head affects regional economic development. Rural welfare maximization depends partly on productive household investment behaviour, and a healthy investment climate can be fostered through a stable inflow of remittances.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".