Beyond the Mozart effect: The influence of musical structure predictability on creativity
Bibliographic record
Abstract
IPDMC has established itself as the leading annual international conference in the domain of innovation management and new product development over the last 30 years.Over these three decades, the innovation management field changed significantly. New frameworks emerged and showed their great power to explain innovation patterns and dynamics.IPDMC 2023 aims to critically discuss how the innovation management field got here and open new discussions on where the field may go in the next decades. The 2023 conference will continue to reflect the achievements, challenges, and development of the field of Innovation and Product Development Management through academic paper presentations, industry engagement, academic keynotes and roundtable discussions on key themes. The conference aims to provide all participants with opportunities to hear, think and contribute creatively to the continuing formation of our field.A broad range of established and emerging topics relevant to innovation and product development will be covered. In addition contributions that address the broader relevance and impact of innovation, technology and product development on society and the environment are welcome.Throughout its history, IPDMC has accepted papers from various disciplines, including organisation studies, marketing, management, technology management, organisational psychology, creativity and design. The conference welcomes all authors who are interested in managerial, policy and social issues related to innovation and product/service development.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".