Bibliographic record
Abstract
Since the inception of the peace process in Nepal, the involvement of international actors has had both enormously constructive and fairly destructive political consequences. At key moments, the role of “outsiders” has enabled political consensus, aided the peace process, and pushed Nepal toward the process of creating a stable, democratic, and just state. Yet there have been other moments when international involvement – either intentionally or unintentionally, directly or indirectly – has led to greater political polarization and contributed to an unstable impasse. This chapter recognizes that the international community in Nepal constitutes a heterogeneous category. Although the United Kingdom and other European donors have paid many of the bills to make the peace process viable, this chapter confines itself to assessing the political role and actions of India (the key regional actor), the United Nations Mission in Nepal (the lead international organization), China (the other neighbor), and the United States (the most powerful player globally). The chapter thereby broadly covers the period between the 12-Point Understanding signed by the Maoists and parties in November 2005 to May 2010 when the tenure of the Constituent Assembly was extended by a year.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".