Knowledge Management in Advanced Technology Industries: An Examination of International Agricultural Biotechnology Clusters
Bibliographic record
Abstract
Innovation—the social process of developing, adapting, and adopting new technologies and products into the economy and society—is being driven by increasingly intensive use of knowledge. Although knowledge is often considered inherently nonrival and nonexcludable, increasing complexity has combined with new private property rights mechanisms to erect barriers to use. One approach to overcoming the challenge of accessing and using knowledge has been for firms and other actors to cluster geographically in a few locations around the world, in order to capture scale and scope economies. This paper offers a theoretical explanation for this agglomeration, examines the extent of clustering in the agricultural biotechnology industry, and investigates one specific cluster—in Saskatoon, Canada—that has sustained success in generating successive innovation. Preliminary results indicate that clusters appear to be prevalent in areas where knowledge is diffuse, complicated, and actively protected. Finally, our results also suggest that regional knowledge management is enhanced through an optimal number of actors operating within the parameters of seven defined cluster-based functions: three primary (science, technology and collective) and four mixed or hybrid activities.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".