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The effects of humor and depression labels on reactions to social comments

2011· article· en· W2156947844 on OpenAlex
M. Sol Ibarra-Rovillard, Nicholas A. Kuiper

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScandinavian Journal of Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyDepression (economics)Clinical psychologyPsychoanalysisSocial psychologyKeynesian economics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.391
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it