Good practices in mission-oriented innovation strategies and their implementation
Bibliographic record
Abstract
Modern innovation policies should target in equal measure both economic competitiveness and societal progress. They should be informed by ambitious, overarching principles-based strategies that enable us to formulate specific political goals, or missions. We also need governance structures that allow for the agile, participatory and inclusive implementation of innovation policy measures. Presenting good examples of such strategies and structures at work in the Netherlands, United Kingdom, Sweden, Canada and Japan, our study examines what these examples have to offer in terms of lessons learned that are relevant for Germany and Europe. The results of this study are published as a results paper within the framework of the Reinhard Mohn Prize 2020 project, “Fostering Innovation. Unlocking potential.” This Bertelsmann Stiftung project is tasked with identifying promising mechanisms, institutions and strategies that could be applied to efforts advancing innovative capacity in Germany and Europe. The twofold aim of such efforts is to ensure, for one, that we remain technologically – and thus economically – competitive. But just as important is the need to address societal challenges while ensuring humane, democratic and inclusive economic development. With this vision in mind, the Bertelsmann Stiftung conducted an extensive international good-practice research study and, in cooperation with the Fraunhofer Institute for Systems and Innovation Research ISI, bundled the findings into four so-called results papers. Additional results papers in the “Innovation for Transformation” series address issues such as Networking and exchange in mission-oriented innovation processes, Addressing societal challenges through disruptive technologies and Fostering innovative startups in pre-seed phase. The final publication in this series, An agenda for the future: Innovation for transformation, presents a summary of the results papers’ overarching conclusions.
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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 | 0.156 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".