Bridging a cultural divide: strengthening similarities and managing differences in university-industry relationships
Bibliographic record
Abstract
Many policy makers view universities as economic agents that drive the innovations responsible for increased productivity and economic growth. Initiatives to harness this potential currently focus on commercializing academic research by protecting, managing, and licensing intellectual property. However, many recognize that traditional academic norms and practices can impede these activities. Consequently, some view the cultural boundaries between universities and firms as obstacles to effective knowledge transfer and argue that universities should alter these boundaries by creating commercial incentives to support "entrepreneurial academics." To create the appropriate incentives, policy makers need to understand how academic culture influences the creation of useful knowledge and how the differences between universities and firms influence collaboration. This dissertation addresses this need by investigating how the cultural and physical boundaries between researchers at the University of Toronto and their industry partners influence knowledge sharing within three collaborative programs: the IBM Centre for Advanced Studies, the Nortel Institute for Telecommunications, and the Bell University Labs. This includes an investigation into the benefits that motivate collaboration and the mechanisms through which the partners create, sustain, and conclude partnerships. This study finds that firms and universities share a number of interests and practices. These similarities foster greater understanding and trust between the partners, which facilitates collaboration. Resource sharing strengthens these similarities and firms that invest personnel, knowledge, materials, and data in a partnership increase effective communication and knowledge exchange. This study also finds that academic and firm partners exhibit distinct interests and practices that strongly influence knowledge transfer. These differences are an incentive as well as an impediment to collaboration. Conflicting norms and practices can create tensions but they also promote the development of complementary resources. Universities and firms collaborate because each brings distinct, though complementary, resources into the partnership. Since the norms and practices of each partner shape these distinctions, attempts to diminish cultural differences may partially erode the incentives to collaborate. Overemphasizing commercialization within universities may also impede the valuable informal knowledge exchanges that take place between partners. Managing tensions through third-party mediation and more effective communication channels promotes knowledge transfer more effectively than minimizing cultural differences.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".