Group creativity in context: multilevel effects of linguistic and cultural diversity
Bibliographic record
Abstract
This article introduces a novel approach to studying the effects of linguistic and cultural diversity on group creativity. A theoretical model of ecological diversity is adapted to the group level, distinguishing between intra-individual diversity (the variety of experiences and skills within an individual) and inter-individual diversity (differences among group members). To test this model, a quasi-experimental design was implemented with 116 groups of 2 to 4 participants collaborating on three distinct creative tasks. Results indicate that group creativity is largely independent of both individual creativity and intra-individual diversity. The influence of inter-individual diversity yielded mixed outcomes: cultural differences among group members negatively affected creativity and cohesion, whereas linguistic differences had a positive effect. In multivariate analyses, however, group creativity was primarily determined by group cohesion, group size, and the average intelligence of members. Overall, the findings suggest that inter-individual diversity plays a more important role than intra-individual diversity, linguistic diversity is more beneficial than cultural diversity, deep cultural differences are more valuable than surface-level traits, and visible cultural diversity may require strategies to strengthen cohesion, such as reducing team size.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".