Innovation and value creation in university–industry research centres in the Canadian forest products industry
Bibliographic record
Abstract
As the forest products industry evolves into a modern industry based on cutting-edge industrial and management research, the prevalence and importance of university research centres have gained importance. Although there has been increased funding and attention given to university–industry research centres from policy makers and researchers, little is still known about the benefits or value that these collaborations provide and create for firms. Applied academic research requires the active participation of researchers and practitioners. In the Canadian forest products industry, there are other important actors that need to be considered, the federal and provincial governments as owners and regulators of the resource and funders of research and development projects and intermediary organisations who are often charged with transforming academic results into tools and methods able to be implemented into industry firms. This paper presents the results of three comparative case studies of university–industry research centres operating in the Canadian forest products industry through an exploration of their knowledge and technology transfer processes. The goal is to better understand the value that has been created for the four main groups of actors involved though informal and formal transfer processes and which processes are best suited for different types of knowledge.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".