CIVIC CAPITAL IN THE WATERLOO REGION: Enabling Regional Economic Governance (Working Paper)
Bibliographic record
Abstract
The story of the Waterloo Region is straddles the threshold between myth and reality. It is a story of a small town with a remarkably diverse and dynamic economy. It is a region with world class educational institutions that produce a workforce of highly trained personnel as well as an impressive amount of highly successful spin off firms. The University of Waterloo accounts for 22 % of research commercialization that happens at all universities across Canada (Klugman, 2005). It is a region driven by the dynamo of an innovative ICT cluster, visionary leadership, strong ties between firms and the university, with a globally recognized brand. It is a region characterized by strong industry associations, robust stocks of social capital and associational governance. However, a closer look at these claims reveals several important caveats. For example, ties between firms within the ICT cluster are based on the “how-to ” of doing business, not collaborative research and development projects or proprietary knowledge exchange (Nelles et al, 2005; Bramwell et al, 2004; Bramwell et al, forthcoming). Recent research suggests that the role of the University of Waterloo has shifted from progenitor of high tech spin off firms and generator of commercializable knowledge to a
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".