Why Is Ethnocentrism More Common Than Humanitarianism
Bibliographic record
Abstract
A compelling agent-based computer simulation suggests that ethnocentrism, often thought to rely on complex social cognition and learning, may have arisen through biological evolution (Hammond & Axelrod, 2006).From a neutral start, ethnocentric strategies evolve to dominate other possible strategies (selfish, traitorous, and humanitarian) that differentiate patterns of cooperation with in-group and outgroup agents.We present new analyses and simulations to clarify and explain this outcome by formulating and testing two hypotheses for explaining how ethnocentrism eventually dominates its closest competitor, humanitarianism.Results indicate support for the direct hypothesis that ethnocentrics exploit humanitarian cooperation along the frontiers between ethnocentric and humanitarian groups as world population saturates.We find very little support for the contrasting freerider-suppression hypothesis that ethnocentrics are better than humanitarians at suppressing non-cooperating free riders, although both hypotheses correctly predict a close temporal relation between population saturation and ethnocentric dominance.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".