Human Life History Dimensions in Reproductive Strategies Are Intuitive Across Cultures
Bibliographic record
Abstract
Psychological research has been criticized for its extensive use of American university students to make broad claims about human psychology and behavior. Critics recommend a broader base of participants because there is substantial variability in experimental results across populations, and North American and Western European psychology pool participants may be outliers in comparison with the rest of the species. This challenge is especially pertinent for claims of species-universal evolved psychological architecture. One such claim has been made regarding recognition of human life history strategies. For example, previous research demonstrates that North American women and men can identify male and female characters with fast (high mating effort, low parental investment) and slow (low mating effort, high parental investment) life history strategies, make accurate predictions about their behavioral tendencies, and respond to them in ways that would facilitate participants’ own reproductive success. The current project validates the understanding of fundamental life history dimensions across a wide range of cultures, therefore supporting the idea that there is a universality in human’s ability to use, and perceive others’ use of, life history strategies. Results for each language sample replicated patterns from North American participants. Ratings for characters clustered into two dimensions, mating effort and parental investment. Items most central to the theoretical constructs had the highest factor loadings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".