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Record W7163023609 · doi:10.3126/njg.v2i1.51456

Land management and human resource development policies in Nepal

2003· article· W7163023609 on OpenAlexaff
Hari Prasad, Mahendra Prasad Sigdel

Bibliographic record

VenueNepalese journal of geoinformatics/Journal of geoinformatics Nepal · 2003
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutions3v Geomatics (Canada)
Fundersnot available
KeywordsLivelihoodLand managementNatural resourceLand useContext (archaeology)NepaliNatural resource managementSustainable developmentHuman resources

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.223
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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