No person is an island: The effects of group characteristics on individual trait expression
Bibliographic record
Abstract
Summary Although most researchers now espouse a person‐by‐situation interactionist approach, there remains much work to be carried out to fully understand how different features of the environment interact with personality to influence behavior. Thus, this study sought to examine the moderating effects of three group‐level constructs on the relationships between two personality traits (conscientiousness and extraversion) and individual performance and counterproductive behaviors. Specifically, using trait activation theory as an organizing framework, we considered the moderating effects of the following: (i) a previously unexamined construct called core group evaluations (CGEs); (ii) group conscientiousness composition; and (iii) group extraversion composition. Data were obtained from a sample of university football players (N = 225–252 from 40 groups). The results indicated that CGEs moderated the relationships between individual conscientiousness and both performance (subjective) and counterproductive behaviors. Group conscientiousness composition also moderated the relationships between individual conscientiousness and both performance (objective and subjective) and counterproductive behaviors. Lastly, group extraversion composition moderated the relationship between individual extraversion and counterproductive behaviors. These findings highlight the importance of considering a team's CGEs, as well as the personality composition of team members when investigating the effects of conscientiousness and extraversion on individual performance and counterproductive behaviors. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".