Give Peace a Chance? How Regulatory Foci Influence Organizational Conflict Events in Intractable Conflict Environments
Bibliographic record
Abstract
An intractable conflict environment (ICE) is an extreme context in which deep, unsolvable conflict between groups is central to the actors within it. While non-ICEs are typically assumed in organizational research, ICEs are increasingly common contexts for organizations. Moreover, this context influences peoples' interpretation of potential organizational conflict incidents inside the organization and therefore the likelihood and emotional intensity of organizational conflict events. Whereas a potential conflict incident, such as a disagreement over how to complete a task, may be perceived as benign in a more typical environment, the same incident is more likely to be interpreted as much more negative and emotionally intense when taking place in an ICE, increasing the frequency of conflict events (conflictual behavior). Prior work suggests that, in a typical environment, promotion-framed (achieving positives) interventions reduce conflict more than prevention-framed (avoiding negatives) interventions by temporarily inducing promotion orientations that reduce the likelihood of interpreting conflict. However, we argue an ICE induces a strong prevention focus, which overrides promotion-framed interventions. Instead, we argue in an ICE, a prevention- rather than promotion-framed intervention is likely to be more effective because it "matches" the strong prevention focus. To test this prediction, we examine the difference in number of conflict events in farming cooperatives in rural Ghana (an ICE) after instituting prevention- versus promotion-framed interventions aimed at addressing conflict. Quantitative and qualitative findings from a 9-month field experiment support our hypothesis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".