Social foundations of regional innovation and the role of university spin-offs: The case of Canada's technology triangle
Bibliographic record
Abstract
offs Abstract (ca. 235 words): Drawing from the literature on the role of universities in promoting technology transfer, this paper will develop a regional conceptualization of spin-off processes, and apply it to a regional case study. In doing this, a typology of spin-off firms will be explored, which is based on the following variables: university sponsorship, university involvement in firm formation, character of knowledge applied, and co-localization of the founders. This enables us to analyze the wider impact of universities on technology transfer and regional development. Extending propositions of organizational ecology, we argue that start-up processes and intra-firm adaptations are not competing against one another for superiority in regional growth or selection processes. The argument is developed that new and existing firms can complement one another in a regional context if they succeed in both developing wider regional networks and trans-regional linkages. Our study will focus on the Kitchener and Guelph metropolitan areas about 100 km west of Toronto, sometimes referred to as Canada’s Technology Triangle (CTT), where a larger number of firms related to information technology (IT) have been successfully launched since the 1970s around the activities of the University of Waterloo. This research will investigate
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| 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".