Entrepreneurial Activity and the Dynamics of Technology-based Cluster Development: The Case of Ottawa
Bibliographic record
Abstract
Relatively little attention has been given to the role of entrepreneurial dynamics in the origin and growth of technology clusters. To the extent that the role of entrepreneurship is considered at all, the emphasis is on the locally embedded nature of the process and on the characteristics of the incubator organisation—the immediate past employer of the entrepreneur—and its role as the source of entrepreneurial know how and the technological ideas upon which the new business is based. This paper argues that this is too simplistic a view. There are two strands to the argument. First, entrepreneurs are not 'local'. Rather, they are attracted to technology clusters, or incipient clusters, by a range of magnet organisations (talent attractors). Secondly, entrepreneurs draw on their experience and the networks established during their entire career, working in different organisations and places, and not just on those resulting from their immediate past employment. These processes are illustrated with reference to the technology-based cluster in the Ottawa region of Canada. The paper concludes that the entrepreneurial dynamics underlying cluster development are best understood through an analysis of the role of magnet organisations and the development of a 'talent pool' in supporting the localisation of economic activity in particular spaces over time.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".