How do humble people mitigate group incivility? An examination of the social oil hypothesis of collective humility.
Bibliographic record
Abstract
The present study explores the "social oil" function of humility at the workgroup level. Specifically, we examine collective humility, which reflects observable and consistent patterns of behavioral regularities exhibited by teams, as an explanation for linking group humility composition to reduced group experienced incivility. Drawing on the collective personality perspective, we hypothesize that teams with a high mean on members' humility could facilitate collective humility, in turn reducing group experienced incivility. We further propose two contingency factors that influence this proposed mediation pathway: (1) high group humility diversity could neutralize the positive association between group humility mean and collective humility and (2) an elevated differentiation of group incivility exposure will weaken the negative relationship between collective humility and group experienced incivility. Relying on a time-lagged, multisourced survey from 83 professional work teams, we tested this proposed moderated mediation model and found support for our hypotheses. Our findings have implications for team building and team management regarding personnel selection for humility, reducing the diversity of humility within teams, and explicitly valuing expressed humility in workgroups. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".