EMOTIONS IN THE CONFLICT PROCESS: AN APPLICATION OF THE COGNITIVE APPRAISAL MODEL OF EMOTIONS TO CONFLICT MANAGEMENT
Bibliographic record
Abstract
This study systematically explored the role of a range of emotions in the conflict process. In order to do so, we articulated and tested a typology of discreet conflict‐relevant emotion constructs. Emotions were demarcated by the two dimensions of self‐concern versus other‐concern, and motives to approach or withdraw from the other party or conflict. This typology produced four emotion constructs: hostility (self‐focused approach), self‐conscious emotions (self‐focused avoid), relational positivity (other‐focused approach) and fear (other‐focused avoid). Self‐ and other‐blame and self‐ and other‐concern were proposed as cognitive antecedents of emotions and choice of conflict resolution strategy. We measured individual behavior in the conflict using the conflict resolution strategy scale (Rahim & Magner, 1995). A critical incident survey technique was used to gather data on people's self‐report of a conflict experience. We also explored the contextual effects of conflict issue and relative status. Results brought into question the general hypothesis that emotions mediate the effects of cognitive appraisals on choice of conflict resolution strategy. However, there were consistent patterns in the direct links between cognitions, emotions and conflict resolution strategies that shed further light on the complex relationships between these variables.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".