The effects of humor and depression labels on reactions to social comments
Bibliographic record
Abstract
Humor is generally considered to facilitate social relationships, whereas depression has been related to difficulties in relationships. This study investigated the effects of labeling social comments as humorous, as well as labeling the presenter of the comments as feeling depressed, on recipients' reactions to these comments. To this end, 350 university students were presented with vignettes describing four styles of comments (i.e., affiliative, self-enhancing, self-defeating, and aggressive) made by a casual acquaintance. Participants were randomly assigned to one of four conditions. These conditions varied in terms of whether the four styles of comments were described as humorous or not, and whether the acquaintance making the comments was described as feeling depressed or not. Findings indicated that humor led to more positive reactions. Labeling the acquaintance as depressed led to more negative reactions than when the acquaintance was labeled non-depressed, particularly when the comments were self-defeating. Interestingly, when the acquaintance was described as feeling depressed, affiliative comments made in a humorous fashion led to more positive reactions than did non-humorous affiliative comments. These findings are discussed in terms of the effects of humor and depression on interpersonal interactions.
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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 | 0.019 |
| 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".