Do you know when you are the punchline? Gender-based disparagement humor and target perceptions
Bibliographic record
Abstract
Purpose This paper aims to draw upon extant theory and research to delineate the fundamental factors that impact how women evaluate disparaging humor directed at them. The conceptual framework presented outlines the most fundamental organizational-, interpersonal- and individual-level factors that influence the accuracy of such evaluation. Design/methodology/approach This is a conceptual paper that offers both a review of extant humor and gender research and theory and the presentation of a theoretical model that classifies sources of influence on evaluations of sexist humor from the perspective of the target. Findings Organization-, interpersonal- and individual-level factors are identified as sources of influence on women’s perception and evaluation of sexist humor leveled at them. This classification identifies factors including organizational power dynamics, egalitarian norms, interpersonal trust, target self-esteem and feminist identity. Research limitations/implications This paper offers a conceptual framework to guide future studies in more systematically examining the sources of influence on female targets’ capacity to recognize when they are the “punchline” of sexist humor. Practical implications The conceptual model developed in this paper offers important implications for managers and leaders in organizations in assisting targets to recognize instances of sexist humor directed at them. The aim is to arm potential victims with the knowledge necessary to foster awareness of their treatment in the workplace and to improve the accuracy of evaluation of workplace attitudes that may often nurture a sense of approval or apathy regarding displays of sexist humor. Originality/value This paper presents a novel classification of sources of influence on female targets’ evaluation of sexist humor in the workplace.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".