Bibliographic record
Abstract
In this paper I argue that many sociocultural phenomena are best explained by the comparative (phylogenetic) method, which consists of using information on other species, notably our closest relatives, the nonhuman primates, as a means to understand the evolutionary history and biological underpinnings of human traits. The social phenomena considered here embody theunitary social configuration of humankind, the set of traits common to all human societies. Those traits could not be explained by sociocultural anthropology, or the other social sciences, because even though they have a highly variable cultural content, they are not cultural creations but rather the products of human nature, or natural categories. I argue that some of those traits resulted from the cognitive enhancement of specific primate traits in the course of human evolution and others evolved as by-products of the coalescence of several primate traits, and I illustrate each process with a number of examples. I also show that even though many of those traits are crossculturally universal, they need not be: culture may modulate the expression of primate legacies and produce various sociocultural patterns from the same set of universal biological underpinnings, or biological constants. Finally, I discuss the importance for the social sciences of integrating biological constants in their models and theories even when they seek to explain culturaldifferences.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".