Spatial Distribution of Farm-Family Resources in the Mid-Hills of Nepal
Bibliographic record
Abstract
The location of farm households along the spatial gradient affects resource availability and farmers’ livelihoods. Many socioeconomic variables have strong spatial affinity that would otherwise be overlooked by data aggregation at household levels. The Geographic Information System (GIS) displays and analyzes socioeconomic data that could aid many social researchers in understanding socioeconomic reality influenced by geographical positions. This paper aims to integrate socioeconomic data into a GIS environment. It examines spatial tendencies of farm-family resources in the mid-hills of Nepal using spatial and random sampling techniques. Farmers living in relatively flat lands and nearby urban centers have small families, higher level of education, farm and family income. In addition, they have small agricultural holdings and engage in commercial farming. Meanwhile, the opposite applies to farmers living in the hills. These spatial differences are related mainly to road, market, and other infrastructure that are crucial for agricultural development and livelihood enhancement. Strong spatial trend in socioeconomic aspects and farm-family resource availability infer the need to focus development activities spatially.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".