Cross-cultural evidence that intergroup conflict heightens preferences for dominant leaders: A 25-country study
Bibliographic record
Abstract
Across societies and across history, seemingly dominant, authoritarian leaders have emerged frequently, often rising to power based on widespread popular support. One prominent theory holds that evolved psychological mechanisms of followership regulate citizens' leadership preferences such that dominant individuals are intuitively attributed leadership qualities when followers face intergroup conflicts like war. A key hypothesis based on this theory is that followers across the world should upregulate their preferences for dominant leaders the more they perceive the present situation as conflict-ridden. From this conflict hypothesis, we generate and test four concrete predictions using a novel dataset including 5008 participants residing in 25 countries from different world regions (consisting of a mix of convenience and approximately representative country-specific samples). Specifically, we combine experimental techniques, validated psychological scales, and macro-level indicators of intergroup conflict to gauge people's preferences for dominant leadership. Across four independent tests, results broadly support the notion that the presence of intergroup conflict increases follower preferences for dominant leaders. Thus, our results provide robust cross-cultural support for the existence of an adaptive, tribal followership psychology, a finding that has various implications for understanding contemporary politics and international relations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".