The Frontier Research in Commercialization and Diffusion of Science
Bibliographic record
Abstract
The symposium on the diffusion and commercialization of science delves into the critical role of scientific knowledge in driving innovation and economic growth. It addresses the challenges and strategies involved in using publicly available scientific knowledge for commercial applications. The symposium brings together researchers studying the commercialization and diffusion processes, focusing on how firms access and utilize scientific knowledge, the role of universities and scientists in the diffusion of scientific knowledge, and the impact of successful commercialization on science itself. These papers range from the development of novel measures to examine the commercial potential of scientific research to exploring the impact of media and social media in disseminating scientific knowledge and investigating the interplay between corporate and academic research in the development of enabling technologies like quantum computing. Given the decline in corporate science over the last few decades, the challenge for firms is effectively leveraging scientific discoveries from universities and research institutions. The papers in the symposium offer important implications for businesses and research institutions striving to bridge the gap between scientific research and commercial application. Commercial Potential of Science and its Realization: Evidence from a Measure Using a LLM Author: Roger Masclans Armengol; Fuqua School of Business, Duke U. Author: Sharique Hasan; Fuqua School of Business, Duke U. Author: Wesley Cohen; Duke U. The Folding Effect: Dimensional Shifts and Reorientation in Organizational Search Author: Sukhun Kang; UC Santa Barbara Escaping the Ivory Tower: Media Coverage and the Commercial Diffusion of Science Author: Saqib Mumtaz; Haas School of Business, UC Berkeley Exploring the Role of Social Media in the Diffusion of Research Author: Yotam Sofer; Copenhagen Business School - Department of Strategy and Innovation Overcoming the Division of Labor in Scientific Research for Complementary Innovation Author: Avi Goldfarb; U. of Toronto, Rotman School of Management Author: Jino Lu; Washington U. in St. Louis, Olin Business School Author: Florenta Teodoridis; California Southern U.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".