Solving Complexities in Creativity Research: Towards a Holistic Understanding across Levels
Bibliographic record
Abstract
Studies on creativity and innovation now span every level from the micro-psychological level of individual creativity, to the macro- system level of innovation of entire societies (Anderson et al. 2014). It appears that despite our increasing knowledge of the factors and the role of the context, our overall understanding of creativity at work remains fragmented around the lens an author chooses. This is problematic, because not only does the study of creativity appears even more complex, but also because we seem to move away from a general understanding of creativity in the workplace. This symposium intends to address challenges of increasingly complex creativity research. To that end, the proposed symposium has a twofold purpose. First, it intends to open discussions of how to synthesize the rich findings across studies at various levels into a more holistic view on creativity at work. Specifically, it highlights cutting-edge research on the underlying mechanisms of creativity at diverse levels of analysis. Despite the diverse lenses the papers use to explain creativity, the symposium attempts to highlight the parallels between the approaches. Second, the symposium aims to provide ideas for future research on how to solve complexities of workplace creativity across levels (Anderson et al. 2014). With the involvement of Jing Zhou as a discussant, the symposium will use this as an ideal opportunity to examine the complexities in workplace creativity from a refined view on the interplay between the individual and contextual factors. Specifically, the symposium consists of four papers, looking at creativity or related behaviors at a distinct level (from the micro-/ individual- up to the macro-/ societal level). Although various perspectives are used, all four papers touch on similar complexities. Bootleging, creative deviance, and formal creative project evolution Presenter: Charalampos Mainemelis; ALBA Graduate Business School Presenter: Evy Sakellariou; Kingston U. London University-firm collaboration for innovation in Chile Presenter: Claudia Contreras Rojas; United Nations Ambidextrous construal level signals and creative idea evaluation Presenter: Jean-Nicolas Reyt; McGill U. Presenter: Stéphane Francioli; New York U. Presenter: Batia Mishan Wiesenfeld; New York U. Balancing novelty & usefulness: Regulatory focus, the role of emotional and cognitive processes Presenter: TIll Nicolas Deuschel; IE Business School Presenter: Jill Waymire Paine; IE Business School
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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 | 0.068 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.049 | 0.060 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.010 | 0.029 |
| 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".