The Shifting Sands of Peacemaking: Challenges of Multiparty Mediation
Bibliographic record
Abstract
Multiparty mediation, which occurs when two or more third parties cooperate or compete in helping antagonists negotiate a conflict settlement, carries both risks and rewards as a conflict management strategy. Cooperating multiple third parties can increase the chances of crafting an agreement, band together to create greater pressure on the conflict parties to reach agreement, and supply outside resources to help implement the negotiated agreement. Competing multiple third parties can undercut each other, prolonging the conflict and allowing antagonists to resist necessary compromises and negotiated concessions. This article examines the changing environment for multiparty mediation and the impact of five changes that affect the practice of mediation. It derives some interim conclusions about where the field is heading and offers some recommendations for making multiparty engagements more effective.
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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 | 0.035 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.079 |
| Scholarly communication | 0.028 | 0.030 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".