Saskatoon's agricultural biotechnology cluster and the Canadian Light Source: an assessment of the potential for cluster extension
Bibliographic record
Abstract
Clusters are a key focus of policy makers worldwide. A successful cluster is often characterised as a 'jig-saw' puzzle (Martin and Sunley, 2002) containing a range of actors (e.g., private firms, research institutes, civic associations, government entities and venture capital firms) and functions (e.g., research and development, services, high quality personnel, finance and networking). The objective of this study is to define and analyse the Saskatoon agricultural biotechnology cluster using new metrics related to functions and assess its capacity to become a broader life science cluster. To do this, the study (1) determines the density of Saskatoon's agricultural biotechnology cluster, (2) examines whether 'innovative' organisations can be determined prior to becoming 'innovative,' and (3) evaluates whether any of the core or central actors in the cluster supply differential functions to innovators. This study first surveyed core actors in Saskatoon to determine their connections with other actors in the industry (within 100 km). This revealed a network density of 15.0%, which supports the assertion that an ag-biotech cluster exists in Saskatoon. 'When the data were disaggregated by function, we discovered that networking had the highest density of all five functions, which suggests that the local cluster is still in the Innovation Stage of industrial development (Lundvall, 1992). Second, when 'innovative' and 'non-innovative' finns were compared, there was no statistically significant externally visible characteristic that would allow anyone ex ante to distinguish between 'innovative' and 'non-innovative' enterprises. Third, Saskatoon's central actors were examined to determine whether they provide differential functions to 'innovative' firms. Only three central actors were significantly linked to supporting highly 'innovative' firms: NRC-IRAP is connected for the provision of research and development; AgWest Biotech is correlated for financial exchanges; and NRC-PBI is significantly offering differential networking services to 'innovative' firms. The study then uses this analysis to infer that the suitable environment - the infrastructure, education programs, community leaders, central actors and government focus - is in place in order to facilitate the extension of the agricultural biotechnology cluster to a broader life science focus, but that existing institutions may need to shift their offerings to ensure they support innovative activity in this new area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".