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Record W2527356824 · doi:10.1287/orsc.2016.1082

A Laughing Matter: Patterns of Laughter and the Effectiveness of Working Dyads

2016· article· en· W2527356824 on OpenAlex
Lu Wang, Lorna Doucet, Mary J. Waller, Karin Sanders, Sybil Phillips

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.

Bibliographic record

VenueOrganization Science · 2016
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsYork University
Fundersnot available
KeywordsLaughterPsychologySocial psychologyCognitive psychologyCommunication

Abstract

fetched live from OpenAlex

Poor communication in teams has been found to result in disappointing team performance. Integrating research on team communication and laughter, we tested hypotheses about the relationship between working dyads’ patterns of laughter and their open communication and effectiveness. We examined two patterns of laughter: shared laughter occurs when both individuals laugh frequently in a dyad, and unshared laughter occurs when one individual in a dyad laughs frequently, but the other does not. Using data collected from 93 flight simulations in two aviation courses, we found that dyads engage in more open communication and are more effective when one member laughs frequently, but the other member does not. In addition, we found that the agreeableness of a dyad member reduces team effectiveness by increasing the likelihood of shared laughter. These results highlight the important role of laughter in team interactions and expand the growing literature on the role of emotions in teams.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.929

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.347
Teacher spread0.319 · 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