Prior-Learning, Cumulative Science Experiences and the Absorptive Capacity of Bio-Entrepreneurs: A Case of the East Midlands Region, England
Bibliographic record
Abstract
In the modern healthcare and medical sectors corporate bio-pharmaceutical firms continue to scale down their in-house research and development (R&D) activities in favour of outsourcing the services to bio-tech ventures. These small but, entrepreneurial research-oriented organisations have increased dramatically. They are predominantly owned by bio-entrepreneurs who are extensively experienced scientists. In the science-based industry they operate in, innovation “ecosystems” consisting of global business and social networks are a common feature. As such, they have to consistently exploit them to complement the knowledge gaps in their enterprises. In that context, the paper sets out to investigate five bio-entrepreneurs currently active in biotechnology within the East Midlands region in England. It particularly examines the role performed by their prior-learning and their cumulative science experiences in recognising, assimilating and productively applying science-related knowledge acquired in their innovation “ecosystems”.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".