Causation and Behavior: The Necessity and Benefits of Incorporating Evolutionary Thinking into Political Science
Bibliographic record
Abstract
Abstract Political science now recognizes that both biological and social factors are significant to the expression of political phenomena. While necessary, this development has significant theoretical and methodological consequences. The recognition of biological and social factors complicates, rather than simplifies, the study of political phenomena by requiring a more complex model of behavioral causation. Objective .To adapt to this complexity, political science must familiarize itself with the study of behavior in the life and evolutionary sciences and adopt a consilient behavioral model. Method .To assist with this development, this article familiarizes political scientists with the principles on causation as they relate to behavior. It also reviews the most common approaches to studying behavioral causation in the evolutionary sciences. Conclusion .The article discusses the practical benefits of incorporating evolutionary thinking into the study of politics, including the importance of evolutionary thinking for problems of omitted variable bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".