Intellectual property strategies, collaboration and technological capabilities: The fuel cell cluster in Vancouver, BC
Bibliographic record
Abstract
This paper describes the development of the fuel cell cluster in Vancouver, Canada, with data collected over three years. This allows to following up the links that come up and the patterns and purposes of collaboration among cluster actors. Knowledge flows through patenting and the university role on the knowledge creation are key issues for this research. Other factors considered are: access to venture capital, characteristics of the city where the cluster is located, and the policies oriented to support its development. The paper is organized in five parts: (i) The ways to collaborate and the links produced between different types of organizations. (ii) The role of customers, suppliers and competitors to produce innovations and the identification of fuel cells market opportunities. Particularly, the paper addresses the role of the university on the fuel cell market development because some differences related to the traditional role reported in the clusters literature were found. (iii) The geographic location of the cluster and the analysis of policies behind the cluster growth. (iv) The intellectual property strategies to protect knowledge and commercialize it at the fuel cell market. (v) The identification of core capabilities that have positioned companies as competitors on the international market
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".