Forms and Functions of the Social Emotions
Bibliographic record
Abstract
In engineering, form follows function. It is therefore difficult to understand an engineered object if one does not examine it in light of its function. Just as understanding the structure of a lock requires understanding the desire to secure valuables, understanding structures engineered by natural selection, including emotion systems, requires hypotheses about adaptive function. Social emotions reliably solved adaptive problems of human sociality. A central function of these emotions appears to be the recalibration of social evaluations in the minds of self and others. For example, the anger system functions to incentivize another individual to value your welfare more highly when you deem the current valuation insufficient; gratitude functions to consolidate a cooperative relationship with another individual when there are indications that the other values your welfare; shame functions to minimize the spread of discrediting information about yourself and the threat of being devalued by others; and pride functions to capitalize on opportunities to become more highly valued by others. Using the lens of social valuation, researchers are now mapping these and other social emotions at a rapid pace, finding striking regularities across industrial and small-scale societies and throughout history.
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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 | 0.005 |
| 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 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".