Connecting creativity and innovation research: Building bridges to cross divides
Bibliographic record
Abstract
Creativity and innovation, while closely related, are concepts often studied within separate academic traditions. Creativity, rooted in psychology, focuses on micro-level processes, whereas innovation, grounded in economics, management science, and organization theory emphasizes macro-level dynamics. This separation has resulted in limited cross-disciplinary dialogue and a fragmented understanding of their interdependencies. In this paper, we advocate for building metaphorical bridges between creativity and innovation research to foster a more integrated understanding of the production of “the novel and useful” knowledge in organizations. We begin by providing a historical overview of both fields, highlighting their origins, key insights, and methodological approaches. Using a framework that maps four research domains in a two (creativity-innovation) by two (micro-macro) table, we identify existing connections and propose pathways for a more integrated theoretical perspective. We underscore the importance of sustaining these bridges, arguing that such integration is crucial for the continued evolution of both fields. By promoting the integration of separate research streams, we aim to enhance conceptual clarity and address complex challenges that require a holistic approach. This paper introduces the special issue “Connecting Creativity and Innovation Research”, outlining future research directions and showcasing contributions that exemplify and advance this integrative effort.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.078 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.012 | 0.074 |
| Scholarly communication | 0.033 | 0.090 |
| Open science | 0.005 | 0.044 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".