Moving beyond the adaptationist paradigm for human evolution, and why it matters
Bibliographic record
Abstract
The Journal of Human Evolution (JHE) was founded 50 years ago when much of the foundation for how we think about human evolution was in place or being put in place, providing the main framework for how we consider our origins today. Here, we will explore historical developments, including early JHE outputs, as they relate to our understanding of the relationship between phenotypic variation and evolutionary process, and use that as a springboard for considering our current understanding of these links as applied to human evolution. We will focus specifically on how the study of variation itself has shifted us away from taxonomic and adaptationist perspectives toward a richer understanding of the processes shaping human evolutionary history, using literature searches and specific test cases to highlight this. We argue that natural selection, gene exchange, genetic drift, and mutation should not be considered individually when considering the production of hominin diversity. In this context, we offer suggestions for future research directions and reflect on this more complex understanding of human evolution and its broader relevance to society. Finally, we end by considering authorship demographics and practices in the last 50 years within JHE and how a shift in these demographics has the potential to reshape the science of human evolution going forward.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| 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".