Understanding scientific creativity: an exploratory creativity scale for organic chemistry
Bibliographic record
Abstract
Creativity is pivotal for innovation across various domains, including science, technology, engineering, and mathematics (STEM). The present study explores domain-specific creativity in organic chemistry by introducing the Divergent Skeletal Formula Task (DSFT) as a novel measure. The DSFT requires participants to generate constitutional isomers of a given molecular formula, providing an objective quantification of creativity based on the rarity and originality of responses. We investigated the correlations between DSFT performance and established creativity indices—the Alternate Uses Task (AUT) and the Divergent Association Task (DAT)—while controlling for age, gender, and fluid intelligence through partial correlation analyses. The results revealed that correlations between DSFT performance and both AUT Creativity and DAT scores were not statistically significant. However, there was a significant positive correlation between DSFT performance and AUT Flexibility, suggesting that cognitive flexibility is a critical component of creativity in chemistry, even when statistically accounting for age, gender, and fluid intelligence. This finding supports the idea of domain-generality in creativity, indicating that cognitive processes underlying general creative thinking, particularly flexibility, are applicable to specific STEM domains like organic chemistry. Thus, insights from studies on general creativity may be valuable for understanding and fostering creativity in specialized fields, offering practical implications for educational and research settings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".