Lessons Learned From Three Iterative Studies on Creativity Interventions
Bibliographic record
Abstract
Abstract Previous work by the authors suggested that performing conflict-processing tasks improved subsequent creative output on the Alternative Uses Test (AUT). Although a positive relationship was established, the number of conflict levels was limited, i.e., previous work included only conflict and no-conflict conditions. Two online follow-up studies included an additional high-conflict level to better understand the relationship between conflict processing and creative performance. These two follow-up studies did not replicate the previous study’s results, but revealed similar, yet non-significant trends. The current paper compares the three studies, emphasizing differences between them, including study environments, instructions, types of tasks used as interventions, and participant backgrounds, etc. Key conclusions relevant to future, particularly online, studies in design creativity and beyond are as follows. Effective in-person studies may not translate well to online studies, where participant distraction and lack of motivation are more difficult to detect, monitor and control. Imposing a minimum number of correct responses to complete study tasks may reduce the effects of distraction and lack of motivation. Without in-person presence of both the researcher and the study participant, enhanced feedback for online responses may promote comprehension of instructions. However, enabling online participants to ask questions directly can further reduce confusion and improve task completion.
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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 | 0.094 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".