Not the Silicon Valley of the North: Leveraging the affordances of Toronto's technology ecosystem to design an inclusive Canada
Bibliographic record
Abstract
Advancement in connected technologies, known as the fourth industrial revolution, is a driver of progress for our generation. The benefits of progress are not evenly distributed, as regions of concentrated technological innovation disrupt industries in other regions, such as in many parts of Canada. In the face of a combination of external and internal factors, Canada is at an inflection point. Canadian leaders in industry, government and university are looking to strengthen Toronto’s innovation ecosystem as a method for reducing the gap in Canada’s technological progress. The technology hub in the San Francisco Bay Area, known as Silicon Valley, is considered the benchmark for an innovation ecosystem. Leaders in Toronto are attempting to replicate its properties in developing Internet applications, with some calling Toronto ‘the Silicon Valley of the North’. In this paper, the author argues that Toronto is not the Silicon Valley of the North by describing innovation ecosystem components and behaviours, examining the components, behaviours and history of Silicon Valley, and comparing the components, behaviours and history of Toronto. Although Silicon Valley currently dominates innovation in consumer applications and Internet technologies, the author argues that the region is really differentiated by its ability to incubate creative destruction cycles—otherwise known as the successful transition between periods of disparate innovations. The author suggests this was made possible with decades of building the region’s entrepreneurial culture, resource mobility, regulation flexibility, and concentration of people, technology and capital. In contrast, Toronto industry is largely concentrated in financial services, tightly-regulated and historically dependent on U.S. innovations. The author recommends that in order for Toronto to thrive as an innovation ecosystem, the region should avoid replicating Silicon Valley’s technology-driven innovation in consumer applications. Instead, Toronto should focus on amplifying the region’s unique properties (its affordances)—including its expertise in finance, its diversity, its relatively open immigration policies and its affinity for government partnerships—to apply Silicon Valley innovations in unlocking the value of revolutionizing entire industries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".