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The Social-Cognitive Functions of Workplace Humor, Gender, on Promotability

2023· article· en· W4385217637 on OpenAlexaff
Shuai Ren, Rick D. Hackett, Abira Reizer

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychologyModerationTraitSocial psychologyPersonalityBig Five personality traitsContext (archaeology)

Abstract

fetched live from OpenAlex

Humor is a pervasive social phenomenon in the workplace context as more and more organizations nowadays are pursuing a fun work environment. While previous literature had examined the evaluations of leader humor, trait humor of employees in the workplace context had not been examined with career advancement prospects, particularly promotability. Our study investigated the effects of both positive and negative traits of workplace humor reported by employees on their promotability evaluated by supervisors using a dyadic work sample in Israel (N=142 dyads) with a cross-sectional design. Interactions between positive/negative workplace humor, employee gender, and supervisors’ gender were examined. The results suggested that employee trait humor is a critical predictor for promotability when gender is incorporated as a moderator, even after controlling for Big Five personality traits and emotional intelligence. In particular, humorous male subordinates are more likely to be perceived as promotable especially if their supervisors are male while humorous female subordinates are less promotable. Moreover, male and female supervisors evaluate promotability associated with workplace humor differently. Our study replicated and extended the earlier studies concerning the gender bias of humor in the workplace from a subordinate perspective, and yielded important implications for leadership emergence and diversity, equity, and inclusion.

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.

How this classification was reachedexpand

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.077
GPT teacher head0.379
Teacher spread0.302 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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