Managing Knowledge and Identity across the Boundary of Academic and Commercial Science
Bibliographic record
Abstract
In the last few decades, institutions of higher learning are being transformed from ivory towers to become engines of regional and national economic development and ‘knowledge businesses’ increasingly focused on producing commercial products for private industry. The role of academics is rapidly shifting as many in the professoriate are becoming ‘captured’ by an ethos of commercialization as they rush to bring the product of their research to the marketplace. Critics of the entrepreneurial paradigm see academics as promoters as well as victims of commercialisation who internalize the pursuit of profit and value of money under the academic capitalist knowledge regime. While some academic researchers have enthusiastically embraced the transformation in the relationship between science and business, and between the academy and industry, many remain firmly committed to academic science, disinterested in pursuing commercial opportunities. Yet, others choose a middle ground and straddle the academic and commercial boundary. The purpose of this paper is to illustrate the role of identity to influence how academic scientists manage the boundary between the world of academic science and commercial science. Drawing from a large sample of Canadian university academic researchers in the applied sciences (n=379), four distinct categories of academic scientists are identified: Type I: Traditional academics who view the realm of academic science and commercial science as distinct and choose to position themselves strictly as academic scientists; Type II: Pragmatic academic hybrids who view academic and commercial science as distinct but decide to strategically pursue industrial links to acquire resources that support their research; Type III: Collaborative academic hybrids who believe in the paramountcy of academic and industry collaborations for the advancement of science; and Type IV: Academic entrepreneurs who abide in the fundamental importance of academic-industry links for application and for commercial exploitation. Results suggest that our researcher categories are further differentiated with respect to the strength of their collaborations with industry, their program of research, the extent of their industry experience, the degree of financial support they receive from industry, the size of their research laboratory, and by their scientific publications and the number of patents and licenses they hold from their research.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".