When a Smile Changes into Evil: Pitfalls of Smiles Following Social Exclusion
Bibliographic record
Abstract
People have a fundamental and a critical need to belong. Social exclusion impairs this need and rejectedindividuals must seek to regain acceptance from others. It is known that such individuals show an increasedpreference for smiles. On the other hand, social exclusion sometimes leads to aggression. It is possible that thiscontradiction is modulated by acceptance and the level of control, such that prosocial behavior occurs inresponse to evidence of social affirmation, whereas aggression increases in response to reductions in the level ofcontrol. However, little is known about the impact of smiles without social affirmation, or the interactionbetween the effects of smiles and the level of control. In this study, we investigated the effects of such smiles bymanipulating an excluder’s facial expressions (i.e., neutral and smiling faces) and similarity to the participant(i.e., level of control). We hypothesized that smiling excluders that are similar to the participant would increaseaggression in the participant, presumably because being rejected by a similar partner reduces the level of control.In support of our hypothesis, results indicated that when excluders smiled, increased aggression was directed atthose excluders that were similar to the participant. Our findings imply that a smile of an excluder directed at theperson being excluded is one of the risk factors for aggressive behaviors in the excluded person.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".