So, You Have an Idea, What Next? Exploring Creativity After Initial Idea Generation
Bibliographic record
Abstract
Creativity—defined as the generation of novel and useful ideas—is often considered the starting point of a creative process that starts with initial idea generation and ends with idea implementation in an organizational or social context. Decades of research demonstrate that creativity propels careers and drives performance. However, more recently, creativity scholars are recognizing the literature’s myopic focus on idea generation alone (i.e., the first step of the creative process). As a consequence, the literature knows less about the activities of the creative process that occur between initial idea generation and eventual idea implementation. Addressing this gap, our symposium features five papers that each investigate a different aspect of the creative process after initial idea generation. We explore aspects of developing idea, promoting/selling ideas, and iterating through the creative process. Collectively, these papers deepen our understanding of the path creative ideas traverse between generation and implementation. Elaborative Play: Crystallizing Nascent Ideas in Circus R&D Groups Author: Mel Yingying Hua; U. College London Author: Colin Muneo Fisher; UCL School of Management Author: Sarah Harvey; UCL School of Management Idea Vitality: An Inductive Study of Group Idea Elaboration Author: Brian J. Lucas; Cornell U. Author: Celia Chui; HEC Montreal The Double-Edged Sword of Socially Active Champions Author: Pier Vittorio Mannucci; Bocconi U. Author: Jill Perry-Smith; Emory U. Individualism-Collectivism Norms and Responses to Idea Theft: A cross-situational leniency effect Author: Lillien M. Ellis; U. of Virginia Darden School of Business Author: Jack Anthony Goncalo; U. of Illinois at Urbana-Champaign Learning to Sustain Success in Creative Industries: The Enduring Impact of Initial Novelty Author: Justin M. Berg; Stanford Graduate School of Business
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".