Land management and human resource development policies in Nepal
Bibliographic record
Abstract
Nepali life is closely dependent on land resources. Though small in size, Nepal is endowed with multiple land resources more than enough to provide good livelihood to the whole population if they are properly harnessed and utilized. Spatial diversity in terms of climate, topography and associated bio-domains is enough to illustrate Nepal's richness in resources. In order to utilize those resources, first thing for Nepal to do is to have a sustainable vision and mission based on reliable, disaggregated and organized information for harnessing of the resources towards sustainable livelihood. Such system can not be put into place in the absence of human resources capable of doing in-depth survey of lands in relation with human beings and understand inherent resources therein. Nepal has, however, not yet oriented its HRD policies towards this direction. Focus is still either on general education or engineering trades other than surveying. Whether it relates to land reform, natural resource management or agricultural development, the course of Nepal's development cannot be directed towards sustainable livelihood unless the HRD policies place due emphasis on developing quality land surveyors and land managers. This paper tries to analyse the present HRD policies of the country in the context of surveying and land management.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".