Addressing Grand Challenges Through the Entrepreneurial Capabilities of Commercialization Postdocs
Bibliographic record
Abstract
Drawing on the triple helix model, which views the entrepreneurial university as the leading actor in a knowledge-based society, this study uncovers micro-level aspects of the knowledge transfer process linking academic science to industry through the mechanism of commercialization postdoctoral fellowships. We provide primary and secondary evidence from six commercialization-focussed STEM postdoctoral training programs across the US, Canada, and the UK, and analyze these case studies to reveal five key themes. Further interviews (50 in total) and a research workshop elucidate salient features of commercialization-focussed STEM postdoctoral training and funding to address grand challenges from the commercialization of university research. All the commercialization postdoctoral programs examined focused on a venture-founder path, and some program components hindered the development of solutions to grand challenges. Beyond the venture path, we provide evidence on the value of two alternative pathways for science commercialization, both of which greatly enhance academic engagement. An industry champions’ pathway, which guides postdoctoral fellows to rapidly translate academic inventions through roles in established firms and provides science innovation “receptor capacity” in industry. Additionally, a longer-term translational scientists’ pathway shapes the perspective of academic scientists in universities who can co-found multiple science-based ventures through their academic labs across their career.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".