A benchmark analysis of Canadian clean technology commercialization accelerators
Bibliographic record
Abstract
Although the size of the Canadian clean energy market is small, high R&D capacity and clean-tech ventures delivering emerging clean energy technologies could potentially make Canada a global leader in supplying direct products, services and infrastructure to clean energy markets. Technology commercialization centres are of vital importance in facilitating and accelerating the transfer of academic and applied research to create and support technology-based ventures. However, there is a lack of clarity around the governance, performance, operation, and business model of such organizations. In order to develop and implement the best business practices for Clean Energy Commercialization Accelerators (CECAs), this paper explores different business operational models which were adopted by different non-profit clean energy commercialization organizations. A two-stage approach was employed. In the first stage, over fifteen organizations (including twelve non-profit organizations and three university research parks) in Canada, the U.S., and Europe were selected for benchmark analysis. Four distinct business operational models emerge based upon an in-depth analysis: incubation focused, technology-enabled, market-enabled, and strategic partnership. Thereafter, a typology of organizations is proposed, based on four discriminating models: governance, finance, operation, and revenue. This typological analysis is then employed to unravel best business practices for CECAs, in view of governance structure, management practice, community impacts, overall business model and performance, strategic plan, and operation. © 2012 IEEE..
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".