Sector connectors, specialists and scrappers: How cities use civic capital to compete in high-technology markets
Bibliographic record
Abstract
This article uses three cities in the same Canadian province (Ontario): Toronto, Ottawa and Waterloo, to examine how regions compete in high-technology markets. We find that regions use civic capital to leverage new, technological windows of opportunity, but they do so in very different ways. Tracing Toronto's evolution from a marketing hub for foreign multinationals into a centre for entrepreneurship, we illustrate how weak ties and cross-sectoral buzz created a 'super connector', scaling high-technology firms in a wide variety of areas. In Ottawa, task-specific cooperation in R&D, education and specialised infrastructure enabled the region to overcome the disadvantages of its small size as a 'specialist' in a single, capital-intensive niche, telecommunications equipment. Finally, entrepreneurs in Waterloo eschewed task-specific cooperation for peer-to-peer mentoring. By diffusing generic knowledge about how to circumvent the liabilities of smallness, mentoring networks enabled this 'scrapper' city to support smaller start-ups in a broad range of niches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".