Exploring the trade-off between quality and fairness in human partner choice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Partner choice is an important force underpinning cooperation in humans and other animals. Nevertheless, the mechanisms individuals use to evaluate and discriminate among partners who vary across different dimensions are poorly understood. Generally, individuals are expected to prefer partners who are both able and willing to invest in cooperation but how do individuals prioritize the ability over willingness to invest when these characteristics are opposed to one another? We used a modified Dictator Game to tackle this question. Choosers evaluated partners varying in quality (proxied by wealth) and fairness, in conditions when wealth was relatively stable or liable to change. When both partners were equally fair (or unfair), choosers typically preferred the richer partner. Nevertheless, when asked to choose between a rich-stingy and a poor-fair partner, choosers prioritized fairness over wealth-with this preference being particularly pronounced when wealth was unstable. The implications of these findings for real-world partner choice are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it