Pushing the Boundaries: New Frontiersin Conflict Resolution and Collaboration
Bibliographic record
Abstract
The research papers in this volume were initially presented at a conference, entitled 'Cutting Edge Theories and Recent Developments in Conflict Resolution', which celebrated the 20th anniversary of the Program on the Analysis and Resolution of Conflict (PARC). Presenters were encouraged to submit their papers for consideration, and following a rigorous peer review and revision process, nine articles were accepted. The volume explores some of the major themes of conflict analysis, including how powerful dominant discourses can both soothe and exacerbate conflict, the role of civic organizations in promoting peace and incubating democratic principles, the ways in which different forms of dialogue are used to heal historically dysfunctional inter-group relations, and the importance of a deeply institutional, structural understanding of ethnocentrism and racism.The authors conducted their research in several different countries - the U.S., Canada, Bosnia, and Northern Ireland - and used a wide range of analytical techniques including in-depth interviews, surveys, and document analysis. What holds them together is the rigorous tie they make between theory and empirical data. Some authors have built conflict theory inductively, based on their own research and/or secondary sources (e.g. Keles, Coy, et al, and Funk-Unrau), while others have tested existing models with empirical data (e.g. Hemmer, Getha-Taylor, and Pincock). These articles collectively make a solid contribution to theoretical development in the conflict analysis field
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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 | 0.045 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.017 | 0.069 |
| Scholarly communication | 0.059 | 0.072 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".