The effects of social vs. asocial threats on group cooperation and manipulation of perceived threats
Bibliographic record
Abstract
Individuals benefit from maintaining the well-being of their social groups and helping their groups to survive threats such as intergroup competition, harsh environments and epidemics. Correspondingly, much research shows that groups cooperate more when competing against other groups. However, 'social' threats (i.e. outgroups) should elicit stronger cooperation than 'asocial' threats (e.g. environments, diseases) because (a) social losses involve a competitor's gain and (b) a strong cooperative reaction to defend the group may deter future outgroup threats. We tested this prediction in a multiround public goods game where groups faced periodic risks of failure (i.e. loss of earnings) which could be overcome by sufficient cooperation. This threat of failure was framed as either a social threat (intergroup competition) or an asocial threat (harsh environment). We find that cooperation was higher in response to social threats than asocial threats. We also examined participants' willingness to manipulate apparent threats to the group: participants raised the perceived threat level similarly for social and asocial threats, but high-ranking participants increased the appearance of social threats more than low-ranking participants did. These results show that people treat social threats differently than asocial threats, and support previous work on leaders' willingness to manipulate perceived threats.
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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 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".