Bibliographic record
Abstract
In a public statement on 12 June, 2021, five former Prime Ministers of Nepal expressed their concerns over foreign intervention in Nepal’s internal affairs. Based on recent literature in the area of international relations arguing that small states are being coerced to choose the geopolitical interest of the superpowers; similarly, in the conviction that Nepal has been facing a trilateral push, this paper argues that the push and pull factors regarding the same have remain unexplored. In this context, this paper primarily deals with the question of how the power contestation between big players has impacted Nepal’s national security. The objective of this research is threefold: first, to probe how the strategic engagement of big powers in Nepal augmented by the new cold war has become explicit; second, to identify how the geopolitical position of Nepal has played an imperative role in such interference; third, to investigate the domestic factors other than geography responsible for making such engagements possible. In light of the new cold war, the research adds knowledge to the existing scholarship on the newer geopolitical developments in the Himalayan nation by focusing on how multiple variables are inviting such interventions to weaken Nepal’s national security. The article will also posit how these interferences would problematize Nepal's ‘survival strategy’ and the steps that need to be taken to minimize the risks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".