Technological innovation in Canada: a comparison of independent entrepreneurs and corporate innovators
Bibliographic record
Abstract
This paper compares a sample of 124 independent high–technology entrepreneurs with 112 corporate entrepreneurs (intrapreneurs) involved in developing and introducing high–tech innovations across Canada. The study investigates the general management and technical problems faced by these entrepreneurs and contrasts their approaches to such issues as market research, financing and moving from prototype to mass production. The approaches used by the two groups in analysing their markets, deciding on manufacturing facilities and financing of innovations are compared and contrasted. In general, the independent entrepreneurs were technically trained, usually possessing engineering training and no general management training or experience. Corporate entrepreneurs were as likely to come from management backgrounds as technical, or else supported their lack of general management skills by adding people to their team with skills in marketing, finance and manufacturing. Their problems were more often those of defending their ideas to management within the corporation, obtaining funding and other resources within the firm, and finding a corporate mentor to assist them in such areas as fighting political battles, providing rewards and incentives for team members, and creating the right overall environment or culture for innovation within the corporation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".