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Record W4403228707 · doi:10.5334/irsp.935

The Double-Edged Sword: Unraveling the Dual Outcomes of Workplace Humor on the Social Identity of Employees

2024· article· en· W4403228707 on OpenAlexaff
Sana Mumtaz

Bibliographic record

VenueInternational Review of Social Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsRegional Municipality of Niagara
Fundersnot available
KeywordsSWORDDual (grammatical number)Identity (music)Social identity theoryPsychologyBusinessSociologySocial psychologyArtAestheticsComputer scienceLiteratureWorld Wide WebSocial group

Abstract

fetched live from OpenAlex

Building on the social identity theory, this conceptual article proposes a process model to understand the linkage between workplace humor and the social identity change experiences of employees. Further, it identifies the underlying mechanisms and conditions which impact the positive and negative identity changes during this process. Based on the proposed model, it is suggested that exposure to negative workplace humor is likely to lead to employee surface acting particularly when the need for social affiliation is high among individuals. On the one hand, identity synergy would facilitate positive emotions and psychological safety and is likely to support improved voice behavior in employees. On the other hand, perceptions of identity conflict would trigger negative emotions and lead to emotional exhaustion and expressed rudeness at the workplace; such individuals would engage in deviant workplace behaviors because of persistent negative experiences. Overall, the proposed conceptual model proposes a thorough relational process model unveiling socio-psychological outcomes of negative workplace humor and needs to be tested in multiple contexts to unveil the role of novel conditional factors impacting internalized change experiences of employees.

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: none
Teacher disagreement score0.893
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.486
Teacher spread0.409 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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