Team Creativity and Innovation Research: Introducing Diverse Perspectives and Novel Insights
Bibliographic record
Abstract
In recent years, there has been a growing interest in understanding team creativity and team innovation. Despite the advances we have made in understanding the drivers of creativity and innovation at the team level, research calls for new perspectives to gain a better understanding of team creativity and innovation (Gilson et al., 2015). The current symposium showcases four pieces of research on team creativity/innovation and aims to provide a comprehensive understanding of current developments in this area of study. In this symposium, we aim to facilitate communication between team creativity and innovation researchers using different methodologies to fill the gaps in current knowledge and inform the latest research interests on this topic. A Dynamic Goal Perspective on Team Innovative Performance. Author: Jie Li; Wilfrid Laurier U. Author: Yaping Gong; The Hong Kong U. of Science and Technology Author: Jingzhou Pan; Tianjin U. Sustaining Group Creativity over Time: The Case of the Monty Python. Author: Pier Vittorio Mannucci; Bocconi U. Author: Lorenzo Bertocchi; Department of Management and Technology, Bocconi U. Leaders’ dominance- and prestige-orientations and team innovative performance Author: Wookje Sung; Hong Kong Baptist U. Author: Inseong Jeong; Lingnan U. Author: Sejin Keem; Portland State U. Author: Shungjae Shin; Portland State U. Author: Wen Wu; Beijing Jiaotong U. A Meta-Analysis of Team Climate and Team Innovation Author: Claire Zhang; Georgia State U. Author: Kris Byron; Georgia State U. Author: Sejin Keem; Portland State U. Author: Tanja R. Darden; Towson U.
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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 | 0.040 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| 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".