Farming Differentiation in the Rural-urban Interface of the Middle Mountains, Nepal: Application of Analytic Hierarchy Process (AHP) Modeling
Bibliographic record
Abstract
This article investigates the dominant factors of farming differentiation in the rural-urban interface of the densely populated Kathmandu valley using analytic hierarchy process (AHP). Rural-urban interface of Kathmandu valley is an important vegetable production pocket supplying a large amount in the city core. While subsistence farming in the rural area is characterized traditional farming integrating livestock, forestry with agriculture; intensification in the urban fringe is characterized by triple crop rotations, intensive vegetable production and market oriented modern farming. Seven factors which were supposed to cause farming variation in the interface were incorporated in the AHP framework and were subjected to farmers’ judgment in distinctly delineated three farming zones. These factors played crucial yet different roles in different farming zones. Inaccessibility and use of local resources; higher yield and accessibility and agro-ecological consideration and quality production are the key impacting factors towards subsistence, commercial inorganic and smallholder organic zones respectively. The quantification of the impacting factors of farming differentiation through AHP is an important piece of information that will contribute to modeling farming in the rural-urban interface in developing countries which represent diversity of farming practices and rapidly changing land use pattern.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".