Patterns in Electronic Brainstorming
Bibliographic record
Abstract
Previous research has shown that some groups using electronic brainstorming generate more unique ideas than groups using nominal group brainstorming, while others do not. This study examined two factors through which group size may affect brainstorming performance: synergy and social loafing. Groups brainstormed using three techniques to manipulate synergy and two group sizes to manipulate social loafing. We found no social loafing effects. There were significant differences in synergy, but not the ones we had theorized. Instead, we found a time effect: nominal brainstorming groups that received no synergy from the ideas of others produced more ideas than electronic groups in the first time period and fewer ideas in the last time period. We conclude that synergy from the ideas of others is only important when groups brainstorm for longer time periods and may have a harder time generating ideas. We also conclude that electronic brainstorming groups, whether in the field or in the research laboratory, should be given at least 30 minutes to work on tasks or else they will be unlikely to develop synergy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.022 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".