The emotion-valuation constellation: multiple emotions are governed by a common grammar of social valuation
Bibliographic record
Abstract
Social emotions are hypothesized to be adaptations designed by selection to solve adaptiveproblems pertaining to social valuation—the disposition to attend to, associate with, and aid atarget individual based on her probable contributions to the fitness of the valuer. To steerbetween effectiveness and economy, social emotions need to activate in precise proportion to the local evaluations of the various acts and characteristics that dictate the social value of self and others. Supporting this hypothesis, experiments conducted in the United States and India indicate that five different social emotions all track a common set of valuations. The extent to which people value each of 25 positive characteristics in others predicts the intensities of: pride (if you had those characteristics), anger (if someone failed to acknowledge that you have thosecharacteristics), gratitude (if someone convinced others that you have those characteristics), guilt (if you harmed someone who has those characteristics), and sadness (if someone died who had those characteristics). The five emotions track local valuations (mean r = +.72) and even foreign valuations (mean r = +.70). In addition, cultural differences in emotion are patterned: They follow cultural differences in valuation. These findings suggest that multiple social emotions are governed (in part) by a common architecture of social valuation, that the valuation architecture operates with a substantial degree of universality in its content, and that a unified theoretical framework may explain cross-cultural invariances and cultural differences in emotion.
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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 | 0.002 | 0.008 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".