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Record W3131265741 · doi:10.1515/humor-2020-0082

Understanding the effects of (dis)similarity in affiliative and aggressive humor styles between supervisor and subordinate on LMX and energy

2021· article· en· W3131265741 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHumor - International Journal of Humor Research · 2021
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsHEC Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySimilarity (geometry)Social psychologyPerspective (graphical)SupervisorDevelopmental psychology

Abstract

fetched live from OpenAlex

Abstract Prior studies on humor have primarily focused on the effects of either leader or subordinate humor styles and thus have neglected the influence of (dis)similarity in humor styles between supervisor and subordinate. We draw on the similarity-attraction perspective to suggest that (dis)similarity in supervisor’s and subordinate’s affiliative and aggressive humor influences workplace energy via the leader-member exchange (LMX). Results show that LMX is higher when leader and subordinate both display high-affiliative and low-aggressive humor behaviors. Furthermore, LMX is higher when a low-affiliative humor subordinate is paired with a high-affiliative humor leader and when a high-aggressive humor subordinate is paired with a low-aggressive humor leader. Our findings reveal that LMX mediated the relationship between (dis)similarity in humor styles and employee energy. Taken together, our results contribute to the understanding of the effects of similarity and dissimilarity in humor behaviors in energic relational processes.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.652
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.211
GPT teacher head0.452
Teacher spread0.241 · 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