Research and technology organizations as entrepreneurship instruments: the case of the Institut National d’Optique in the Canadian optics and photonics industry
Bibliographic record
Abstract
Abstract Research and technology organizations (RTOs) are studied in the innovation policy literature mainly as providers of R&D services and as intermediaries between universities and the private sector. Through the case of the Institut National d’Optique (INO), Canada’s leading RTO in the optics and photonics industry, we argue that RTOs can also act as entrepreneurs by generating technologies and commercializing them through licensing, technology transfers and spin-offs. By analyzing the broad range of activities undertaken by INO, we also discuss what characteristics make some RTOs more likely to embrace entrepreneurship than others. Those characteristics include the following: renewed access to government funding to build a strong in-house research infrastructure and scientific workforce; strategic R&D planning that incorporates commercial objectives and an environment that encourages a culture of entrepreneurship among employees; the ability to act as the driving force of a network of academic, government and private sector organizations. From a policy perspective, the INO case indicates that the main value of using RTOs as entrepreneurship instruments does not lie in profitability but rather in developing dynamic regional systems of innovation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".