David Bowie and the Art of Slow Innovation: A <i>Fast-Second Winner</i> Strategy for Biotechnology and Precision Medicine Global Development
Bibliographic record
Abstract
Original ideas and innovation cannot always be ordered like a courier service and delivered fresh to our desk at 9 am. Yet, most creativity-based organizations, careers, and professions, science and biotechnology innovation included, emphasize the speed as the prevailing ideology. But a narrow focus on speed has several and overlooked shortcomings. For example, it does not offer the opportunity to draw from, and stitch together disparate concepts and practices for truly disruptive innovation. Preventing false starts, learning from others' or our own mistakes, and customizing innovations for local community needs are difficult in a speed-hungry innovation ecosystem. We introduce a new strategy, the Fast-Second Winner, specifically in relation to global development of biotechnologies and precision medicine. This à la carte global development strategy envisions a midstream entry into the innovation ecosystem. Moreover, we draw from the works of the late David Bowie who defied rigid classifications as an artist and prolific innovator, and introduce the concept and practice of slow innovation that bodes well with the Fast-Second Winner strategy. A type of slow innovation, the Fast-Second Winner is actually fast and sustainable in the long term, and efficient by reducing false starts in new precision medicine application contexts and geographies, learning from other innovators' failures, and shaping innovations for the local community needs. The establishment of Centers for Fast-Second Innovation (CFSIs), and their funding, for example, by crowdfunding and other innovative mechanisms, could be timely for omics and precision medicine global development. If precision medicine is about tailoring drug treatments and various health interventions to individuals, we suggest to start from tailoring new ideas, and focus not only on how much we innovate but also what and how we innovate. In principle, the Fast-Second Winner can be applied to omics and other biotechnology responsible development in medical practice or any field of applied innovation.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".