Evolutionary Psychology: Counting Babies or Studying Information‐Processing Mechanisms
Bibliographic record
Abstract
Evolutionary psychology focuses on the study of adaptations. Its practitioners put little credence in the study of reproductive success in recent and current environments, and argue for an information-processing, cost-benefit conception of adaptation. Because ancestral and current environments differ, it is necessary to distinguish between innate and operational adaptations and between concurrently contingent and developmentally contingent behaviors. These distinctions lead to an evolutionary classification of behaviors into true pathologies, pseudopathologies, quasinormal behaviors, and adaptive-culturally-variable behaviors. I argue that a complete study of the functioning of a behavioral adaptation involves modeling ancestral selection pressures, cross-cultural research, experimental studies of mental processes, and studies of the proximate biological correlates of information-processing adaptations. Finally, I claim that evolutionary psychology can help us avoid making both naturalistic and moralistic fallacies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".