The evolution of cultural adaptations: Fijian food taboos protect against dangerous marine toxins
Bibliographic record
Abstract
The application of evolutionary theory to understanding the origins of our species' capacities for social learning has generated key insights into cultural evolution. By focusing on how our psychology has evolved to adaptively extract beliefs and practices by observing others, theorists have hypothesized how social learning can, over generations, give rise to culturally evolved adaptations. While much field research documents the subtle ways in which culturally transmitted beliefs and practices adapt people to their local environments, and much experimental work reveals the predicted patterns of social learning, little research connects real-world adaptive cultural traits to the patterns of transmission predicted by these theories. Addressing this gap, we show how food taboos for pregnant and lactating women in Fiji selectively target the most toxic marine species, effectively reducing a woman's chances of fish poisoning by 30 per cent during pregnancy and 60 per cent during breastfeeding. We further analyse how these taboos are transmitted, showing support for cultural evolutionary models that combine familial transmission with selective learning from locally prestigious individuals. In addition, we explore how particular aspects of human cognitive processes increase the frequency of some non-adaptive taboos. This case demonstrates how evolutionary theory can be deployed to explain both adaptive and non-adaptive behavioural patterns.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".