A Review of Environmental Vulnerabilities Related to Nepal’s Graduation Process from Least Developed to a Developing Country Status
Bibliographic record
Abstract
Nepal has long aspired to graduate from the Least Development Country (LDC) to Developing Country category as defined by the United Nations system. Nepal had met two of the three graduating criteria and could have technically graduated from the LDC status in 2015. However, based on the Nepal government’s request to defer the review, the new 2021 assessment by the United Nations Committee for Development Policy (CDP) recommended that the country should graduate from the LDC status by 2026. The graduation requires not only meeting pre-defined development-related thresholds, but also maintaining sustained improvements in at least two consecutive assessments in two of three areas: gross national income (GNI) per capita, human assets index (HAI), and economic and environmental vulnerability index (EnVI). Nepal’s economy is dependent on several environment-related factors such as agriculture, tourism, hydro-power, and natural resources. This economic development is also solidly tied to the environmental well-being of the country. The authors agree with the Nepal government’s desire to graduate from the LDC status. In this paper, we review the graduation process, assess indicators of the Environmental Vulnerability (EnVI), review the current situation with respect to environmental vulnerability, and point out where it needs to develop appropriate goals, policies, and programs to help the country graduate and join the ranks of developing countries.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".