Knowledge networks for new technology–based firms: an international comparison of local entrepreneurship promotion
Why this work is in the frame
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Bibliographic record
Abstract
This paper reports on an international comparison of three organisations established to promote new business start–ups in the USA, UK and Canada. A ‘knowledge–based’ approach is adopted to examine how networks of would–be entrepreneurs interact with networks of experienced entrepreneurs and managers, venture capitalists, technical experts, consultants, IPR lawyers and other specialists. This interaction is promoted and mediated at the local level by the three organisations at the centre of the study: the Austin Technology Incubator (ATI), Texas; Connect, Edinburgh; and the Canadian Environmental Technology Advancement Corporation (CETAC–West) in Canada. These act as local network–nodes or ‘knowledge integrators’, as well as ‘incubating’ new ventures to increase the new business ‘birth rate’ in their respective regions. The comparison is based on interviews and secondary data that describe the initiation, development, operation and local impact of these organisations. Findings stress the importance of the regional context as a source of particular kinds of knowledge and expertise that may promote or inhibit new technology–based business start–ups. In particular: the scale, scope and quality of ideas and business proposals in local networks; the availability of relevant expertise and experience for ‘intelligent selection’ and for successful mentoring; the nature of rewards and incentives for all players; and the importance of local champions or figureheads, are all factors that help explain differences across the example regions. The paper combines a variety of conceptual approaches around the idea of regional knowledge networks which underpin ‘distributed innovation’. Heightened technological and market uncertainty for new technology–based firms places a premium on the ability of entrepreneurs to integrate specialist knowledge and utilise expertise from a variety of local sources. Despite differences in the scale, scope and effectiveness of their efforts we conclude that all three organisations are supporting ‘accelerated learning’ amongst entrepreneurs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it