Wealth or generosity? People choose partners based on whichever is more variable
Bibliographic record
Abstract
Organisms benefit from choosing partners who are willing and able to provide them with benefits (e.g., choose based on warmth, competence, wealth). But which should they prefer in a partner – willingness or abilities? We tested the hypothesis that people will focus on whichever trait is more variable in others: the more variance there is in a trait, the greater the difference there is between the “best” and “worst”, so the more that trait will impact the chooser (all else equal). In two studies, participants saw a range of partners for a hypothetical money distribution task who either varied more in the amount of money they had to distribute (Unequal Wealth condition) or in the percent of their money they gave away (Unequal Generosity condition). Participants had a default preference to know about others' generosity rather than their wealth; this preference was strengthened when others varied more in generosity and weakened when others varied more in wealth. Thus, our study shows that people are sensitive to the amount of population variance on a trait, and flexibly adjust their partner preferences to focus on traits which vary more among others.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".