Bibliographic record
Abstract
Nepal has been experiencing increasing food deficits for the past three decades. Inadequate irrigation facilities, labor shortages, conversion of agricultural lands to other land uses, land degradation, poor agricultural marketing, lack of necessary infrastructures, and poor agricultural productivity are commonly cited constraints to increased agricultural outputs. Cooperatives are one of the three pillars for the economic development of Nepal (public, private, and cooperative). Currently, there are a total of 34,512 cooperatives (13,578 savings and credit, 4,371 multipurpose, 10,921 agriculture, 1,658 milk, 1,423 consumer, 193 fruits and vegetables, 108 tea, 155 coffee, 184 Jadibuti, 93 bee keeping, 143 communication, 128 health, 48 sugarcane, 45 Junar, and 999 other), with a total membership number of 6,305,581 in Nepal. Despite these initiatives, Nepal’s agriculture is experiencing a serious downward spiral. Therefore, it is necessary to immediately identify and implement a theoretically grounded agricultural development framework in order to reverse the downward spiral of agriculture, accelerate economic growth, and achieve fast-paced socio-economic transformation. In this regard, I suggest strengthening cooperatives at the grassroots level for agricultural production within the framework of Asta-Ja (referring to eight Nepali “Ja”- Jal, Jamin, Jungle, Jadibuti, Janashakti, Janawar, Jarajuri, and Jalabayu) and integrating them vertically with public and private businesses at the regional and national level. Agricultural programs and policies need to be formulated so that they would focus locally on Asta-Ja resources and globally on international trade and treaties, global demand and supply, global climate change, the emerging concept of the fourth wave of industrialization, and other pertinent issues within the global context.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".