Bibliographic record
Abstract
Converting scientific expertise into marketable products and services is playing an increasingly important role in the launching of new ventures, the growth of existing firms, and the creation of new jobs. In this article, we explore how the lean startup paradigm, which validates the market for a product with a business model that can sustain subsequent scaling, has led to a new process model to accelerate innovation. We then apply this paradigm to the context of research at universities and other research organizations. The article is based on the assumption that the organizational context matters, and it shows how a deeper understanding of the research context could enable an acceleration of the innovation process. We complement theoretical examples with a case example from VTT Technical Research Institute of Finland. Our findings show that many of the concepts from early-acceleration phases – and the lean startup paradigm – can also be relevant in innovation discussions within the research context. However, the phase of value-proposition discovery is less adequately addressed, and that of growth discovery, with its emphasis on building on a scalable, sustainable business does not seem to be addressed with the presented innovation approaches from the research context. Hence, the entrepreneurial activities at the research context differ from those in startups and internal startups in established organizations.
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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 | 0.024 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".