Relational Conflict Resolution as an Ambicultural Approach to Conflict Management under Ambiguity
Bibliographic record
Abstract
An essential challenge of resolving conflict is to manage relationships. We introduce relational conflict resolution (RCR), define its conceptual dimensions, and provide a theory of its boundary conditions under ambiguity. Using a grounded inductive cross-cultural approach, with both qualitative and quantitative data in three studies conducted in the U.S. and China, we find that RCR– rooted in eastern cultural traditions of holistic, dialectical, and paradoxical thinking, as well as western practices of collaborative problem solving and mediation – is an approach to conflict management that is inclusive and non-confrontational. RCR integrates five facets: 1) the complexity of a long-term perspective, 2) relationship concern, 3) contextual factors, 4) balance of interests, and 5) flexible and informal communication. Although Chinese are more comfortable with RCR, U.S. Americans are also open to such an approach to resolving conflicts in ambiguous and complex organizational settings. In three conditions of ambiguity, both U.S. and Chinese participants preferred RCR more than approaches found in the dual concern model. We propose that RCR bridges and integrates styles of East and West, and offers new insights to managing conflicts in both eastern and western cultures, especially under complex and ambiguous situations.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".