Connecting Creativity and Innovation
Bibliographic record
Abstract
Creativity and innovation are interrelated and mutually reinforcing. Yet, despite their relatedness, micro-level creativity research and organizational-level innovation research have evolved largely independently, resulting in distinct approaches to the understanding of creativity and innovation. Over the years, this has led to the development of parallel streams of research with too little cross- talk. Therefore, the purpose of this panel symposium is to engage a group of panelists in a formal, moderated, interactive discussion about the (1) nature of practical, theoretical, and methodological differences between creativity and innovation as fields of research; (2) the fault-lines these differences have created between creativity and innovation research; (3) the similarities between creativity and innovation research that can bridge existing fault-lines (4) specific practical, theoretical, and methodological ideas for building bridges between creativity and innovation; and (5) new research possibilities opened by more closely connecting creativity and innovation research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".