Bibliographic record
Abstract
Nepal's peace process was both led and driven by Nepalis, but it was also remarkably open to the involvement of a wide range of external actors. This chapter focuses on the most prominent and committed of the international peacemakers involved – among them the Swiss-based nongovernmental organization (NGO), the Centre for Humanitarian Dialogue, that worked in Nepal from 2000–6; the United Nations, whose secretary-general first offered his “good offices” in 2002 and whose presence later grew into a large Office of the UN High Commissioner for Human Rights (OHCHR, established in 2005) and a special political mission, the United Nations Mission in Nepal (UNMIN, formed in 2007); the Carter Center, whose conflict resolution program engaged with Nepal from 2004–6; and the government of Switzerland, which dispatched a special adviser for peacebuilding to Nepal in mid-2005. The chapter also analyzes the critical role played by India, which is explored more fully elsewhere in this volume. None of these external actors came to fill a role of formal facilitation or still less mediation. For the most part, their efforts to encourage dialogue, introduce expertise gleaned from peace processes elsewhere, or provide other unspecified support were undertaken on an entrepreneurial basis, rather than in response to a clear invitation. These activities paralleled not only the presence and activity of national facilitators but also the discrete efforts of a number of individuals within Nepali civil society who encouraged dialogue between Nepal's fractious political actors. These varied actors at times appeared to crowd the peacemaking field in a confusing fashion, yet they were able to make a number of significant contributions. The result was a unique peacemaking mix – masala peacemaking .
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".