It’s the way he tells them (and who is listening): men’s dominance is positively correlated with their preference for jokes told by dominant-sounding men
Bibliographic record
Abstract
While much research has explored humorous exchange in relation to mate choice, recent perspectives have emphasized the importance of humor for monitoring interest within social partnerships more generally. Indeed, given that similarity is thought to be important in the maintenance of social partnerships, we may expect humor appreciation to vary according to the degree of similarity between humor producers and recipients. In the current study we report evidence for such variation that is specific to men’s judgments of other men’s humor. Here we manipulated voice pitch in a set of ‘one-liner’ jokes to create low-pitched and high-pitched versions of men and women telling jokes. A composite measure of men’s own dominance was positively correlated with their preference for jokes told by other men with lowered voice pitch (a vocal cue to dominance). A follow-up study demonstrated that self-reported dominance was positively related to men’s choice of low-pitch men as friends when judging humorous audio clips but not when judging neutral control audio clips, suggesting that humor may be important in mediating the effect of dominance on friendship choice. These studies indicate systematic variation in humor appreciation related to friendship choices which may function to promote cohesion within male partnerships based on status.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".