Developing an innovation engine to make Canada a global leader in cybersecurity
Bibliographic record
Abstract
An engine designed to convert innovation into a country’s global leadership position in a specific product market is examined in this article, using Canada and cybersecurity as an example. Five entities are core to the innovation engine: an ecosystem, a project community, an external community, a platform, and a corporation. The ecosystem is the focus of innovation in firm-specific factors that determine outcomes in global competition; the project community is the focus of innovation in research and development; and the external community is the focus of innovation in resources produced and used by economic actors that operate outside of the focal product market. Strategic intent, governance, resource flows, and organizational agreements bind the five entities together. Operating the innovation engine in Canada is expected to improve the level and quality of prosperity, security, and capacity of Canadians, increase the number of Canadian-based companies that successfully compete globally in cybersecurity product markets, and better protect Canada’s critical infrastructure. Researchers interested in learning how to create, implement, improve, and grow innovation engines will find this article interesting. The article will also be of interest to senior management teams in industry and government, chief information and technology officers, social and policy analysts, academics, and individual citizens who wish to learn how to secure cyberspace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".