Innovating in urban economies : economic transformation in Canadian city-regions
Bibliographic record
Abstract
List of Tables List of Figures Foreword to the Series by David A. Wolfe (University of Toronto, Royal Bank Chair in Public and Economic Policy and Director of the Program on Globalization and Regional Innovation Systems) Acknowledgements Part I: Dynamics of Innovation in City-Regions: Diversity, Specialization and Variety Chapter 1 - Introduction to Innovating in Urban Economies: Economic Transformation in Canadian City-Regions - David A. Wolfe Chapter 2 - Systems of Innovation and Contexts of Creativity: An Assessment of the Knowledge Bases of Canadian City-Regions - Gregory Spencer (University of Toronto, Manager of Local IDEAs) Part II: Diversity, Variety and the Cognitive-Cultural Economy in Large Cities Chapter 3 - Innovation and Toronto's Cognitive-Cultural Economy - Charles H. Davis (Ryerson University, Radio and Television Arts) and Nicholas Mills (Ryerson University, Communication and Culture) Chapter 4 - Living on the Edge: Knowledge Interdependencies of Human Capital Intensive Clusters in Vancouver - Brian Wixted (Simon Fraser University, Research Fellow) and Adam Holbrook Chapter 5 - Innovation and Social Actors in Montreal: Inter-Sectoral Changes of Place-Based Dynamics - Diane-Gabrielle Tremblay (Tele-universite of University of Quebec, Labour Economics and Human Resources Management) and Juan-Luis Klein (Universite du Quebec a Montreal, Geography) Chapter 6 - Firms and their Problems: Systemic Innovation and Related Diversity in Calgary - Cooper Langford (University of Calgary, Communications and Culture), Ben Li (University of Calgary, InnolLab) and Cami Ryan (University of Saskatchewan, Professional Research Associate) Part III: The Specialized Characteristics of Innovation in Medium-Sized Cities Chapter 7 - Innovation in an Ordinary City: Knowledge Flows in London, Ontario - Neil Bradford (Huron University College, University of Western Ontario, Political Science) and Jen Nelles (Hunter College, Urban Affairs and Planning) Chapter 8 - Biotech in Lunch Buckets: The Curious Knowledge Networks in Steeltown - Peter Warrian Chapter 9 - Innovation Linkages in New and Old Economy Sectors in Cambridge-Guelph-Kitchener-Waterloo (Ontario) - Andrew Munro (University of Toronto) and Harald Bathelt (University of Toronto, Political Science) Chapter 10 - Knowledge Flows in the Consulting, Advertising/Design, and Music Sectors of Halifax - Jill L. Grant (Dalhousie University, Planning) Part IV: Innovation for Survival or Growth in Canada's Small Cities Chapter 11 - Social Dynamics, Diversity and Physical Infrastructure in Creative, Innovative Communities: The Saskatoon Case - Peter Phillips (University of Saskatchewan, Public Policy) and Graeme Webb (Simon Fraser University) Chapter 12 - How ICTs and F2F Mediate Knowledge Flows in and Within Moncton - Yves Bourgeois (University of New Brunswick, St. John, Director of Urban and Community Studies Institute) Chapter 13 - Networking Patterns and Performance on Trois-Rivieres City-Region's Firms in the Light of Sectoral and Place Characteristics - Michel Trepanier (Institut National de la Recherche Scientifique), Pierre-Marc Gosselin (Universite d'Ottawa, Sociology and Anthropology) and Rosemarie Dallaire (Adviser for Innovation and SMEs with Innovation et Developpement economique Trois-Rivieres) Part V: The Global Challenge for Innovation in Canadian City-Regions Chapter 14 - Related Variety, Knowledge Platforms and the Challenge for Cities and Regions in the Global Economy - Phil Cooke (University of Wales, Cardiff, Research Professor) Contributors
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".