Research on university research commercialization: approaches, methodologies, and data sources, Professional Development Workshop at Academy of Management Meeting, Montreal, Canada
Bibliographic record
Abstract
Over the past thirty years, the attention toward the commercialization of knowledge developed within universities has increased dramatically. Universities are a prototype for knowledge-intensive organizations and as such they provide lines of discovery and analysis that help scholars understand broader research commercialization dynamics, including open innovation, organizational change and individual entrepreneurial behavior. Yet researchers also face challenges that are specific to this type of organization. This workshop aims to continue the rich discussion from the 2007 and 2008 AOM Annual Meetings. Our aim in this year’s session is to provide the opportunity for participants to learn about approaches, data sources and methods available for the study of universities. Approaches, data and methods used by researchers in this field vary widely. We therefore see this PDW as providing a one-stop-shop for both early career and more experienced faculty researchers to gain an overview on recent developments and future challenges in this area.
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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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.038 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".