Humor styles, peer relationships, and bullying in middle childhood
Bibliographic record
Abstract
Abstract Contemporary approaches to understanding humor have developed models that underscore the importance of both adaptive and maladaptive humor styles. The expression of these humor styles can then impact either positively or negatively on the self or others. One such model, as recently proposed by Rod Martin and his colleagues, outlines four distinct humor styles; namely self-enhancing, affiliative, self-defeating, and aggressive humor. Several studies with both adults and older adolescents provide initial empirical support for this model, including the adaptive aspects of self-enhancing and affiliative humor, as well as the maladaptive components of self-defeating and aggressive humor. However, these four humor styles have yet to be considered with respect to children. As such, the present paper considers how these different humor styles may bear on peer relationships and bullying during middle childhood (ages 6–12). In our examination, we describe how adaptive and maladaptive humor styles may either help or hinder the child's status within a peer group. Special emphasis is directed towards potential relationships between specific humor styles and either peer acceptance or victimization, as well as both direct and indirect forms of bullying. We conclude by describing several potential areas of research that may prove beneficial in furthering our understanding of humor and social relationship issues in middle childhood.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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".