The effects of nonverbal pride and skill on judgements of victory and social influence: a boxing study
Bibliographic record
Abstract
Displaying nonverbal pride after a boxing match leads to judgements of success. However, it is not clear the extent to which this effect generalises nor whether it can override competing information. An experimental design had 214 participants watch two boxing clips that were manipulated so that one was evenly matched and the other had a fighter with an advantage (i.e. demonstrating more skill). Nonverbal behaviour at the completion of the fight varied between fighters (pride versus neutral). When the fight was evenly matched, the fighters displaying nonverbal pride were judged as winning the fight, but the fighter did not garner increased social influence. In contrast, when fighters demonstrated superior skill, the more skilled fighters who displayed neutral postures rather than the less-skilled ones displaying pride were judged as winning the fight, and the skilled fighters garnered increased social influence. These results suggest that in a boxing context, a pride bias works in evenly matched scenarios, but when differences in skill are more clearly present, a skill bias is more pronounced and leads to more social influence. Furthermore, perceptions of skill were associated with judgments of victory across stimuli, suggesting the importance of skill perceptions in such judgments.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".