Culturally valued facial expressions enhance loan request success.
Bibliographic record
Abstract
Why do people share resources with some strangers, but not others? This question becomes increasingly relevant as online platforms that promote lending world-wide proliferate (e.g., www.kiva.org). We predicted that lenders from nations that value excitement and other high-arousal positive states (HAP; e.g., United States) would loan more to borrowers who show excitement in their profile photos because the lenders perceive them to be more affiliative (e.g., trustworthy). As predicted, using naturally occurring Kiva data, lenders from the United States and Canada were more likely to lend money to borrowers (N = 13,500) who showed greater positive arousal (e.g., excitement) than were lenders from East Asian nations (e.g., Taiwan), above and beyond loan features (amount, repayment term; Study 1). In a randomly selected sample of Kiva lenders from 11 nations (N = 658), lenders from nations that valued HAP more were more likely to lend money to borrowers who showed open "excited" versus closed "calm" smiles, above and beyond other socioeconomic and cultural factors (Study 2). Finally, we examined whether cultural differences in lending were related to judgments of affiliation in an experimental study (Study 3, N = 103). Compared with Koreans, European Americans lent more to excited borrowers because they viewed them as more affiliative, regardless of borrowers' race (White, Asian) or sex (male, female). These findings suggest that people use their culture's affective values to decide with whom to share resources, and lend less to borrowers whose emotional expressions do not match those values, regardless of their race or sex. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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; both teacher heads agree on what is shown here.
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".