Reactions to Humorous Comments and Implicit Theories of Humor Styles
Bibliographic record
Abstract
The first two studies investigated reactions to several different types of humorous comments. Participants indicated they would be significantly more likely to continue interacting with a friend who used adaptive self-enhancing or affiliative humor rather than maladaptive aggressive or self-defeating humor; with the most detrimental effects being evident for aggressive humor. Adaptive humorous comments also made recipients feel significantly more positive and less negative about themselves. Humor styles were further investigated in terms of implicit theories about humor. Study 2 indicated that for the self, humor was perceived as being used most often with close friends, followed by family members, romantic partners, casual acquaintances, and least often with teachers. Participants also indicated that affiliative humor was used most frequently for each relationship, followed by self-enhancing humor, self-defeating humor, and then aggressive humor. Study 3 examined the perceived frequency of use for each humor style by others. Participants indicated affiliative humor to be the most frequently used humor style, regardless of the group being rated (people in general, people one knows, family and friends), self-enhancing humor to be the second most frequently used, and the two maladaptive humor styles as being used the least often. Different co-variation patterns for the four humor styles were also found. These findings were then discussed in terms of the strong differential impact of humor styles on the recipients of humorous comments; as well as the implicit theories of humor styles that are evident for self or others.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".