Bibliographic record
Abstract
List of Tables List of Figures Notes on Contributors Acknowledgements Preface. Jill L Grant (Dalhousie University) Part I: Seeking Talent for Innovation Chapter 1. Attracting and Retaining Talent: Evidence from Canada's City-Regions. Meric S. Gertler (University of Toronto), Kate Geddie (Association of Universities and Colleges of Canada), Carolyn Hatch (University of Toronto), and Josephine V. Rekers (Lunds University) Chapter 2. Attracting and Retaining Talent in Canadian Cities: Towards a Holistic View? Tara Vinodrai (University of Waterloo) Part II: Attracting Sector Workers Chapter 3. Cosmopolitanism, Cultural Diversity, and Inclusion: Attracting and Retaining Artistic Talent in Toronto. Deborah Leslie (University of Toronto), Mia A. Hunt (Royal Holloway University of London), and Shauna Brail (University of Toronto) Chapter 4. Screenwriters in Toronto: Centre, Periphery, and Exclusionary Networks in Canadian Screen Storytelling. Charles H. Davis (Ryerson University), Jeremy Shtern (University of Ottawa), Michael Coutanche (Ryerson University), and Elizabeth Godo (Ryerson University) Chapter 5. Satisfaction Guaranteed? Individual Preferences, Experiences, and Mobility. Brian Hracs (Uppsala University) and Kevin Stolarick (Rotman School of Management, University of Toronto) Chapter 6. Those Hermit Artists: Musical Talent on the Edge of the Continent. Jill L Grant (Dalhousie University), Jeffry Haggett (Dalhousie University), and Jesse Morton (Dalhousie University) Part III: Attracting High Technology Workers Chapter 7. Attracting Knowledge Workers and the City Paradigm: Can We Plan for Talent in Montreal? Sebastien Darchen (The University of Queensland) and Diane-Gabrielle Tremblay (Universite du Quebec a Montreal) Chapter 8. Talent, Tolerance, and Community in Saskatoon Peter W.B. Phillips (University of Saskatchewan) and Graeme Webb (Simon Fraser University) Chapter 9. Exploring Creative Talent in a Natural Resource-Based Centre: The Case of Calgary Camille D. Ryan (University of Saskatchewan), Ben Li (University of Calgary) and Cooper H. Langford (University of Calgary) Part IV: Seeking Talent for Small Cities Chapter 10. Kingston and St John's: The Role of Relative Location in Talent Attraction and Retention Josh Lepawsky (Memorial University), Heather Hall (Memorial University), and Betsy Donald (Queen's University) Chapter 11. Small Cities as Talent Accelerators: Talent Mobility and Knowledge Flows in Moncton Yves Bourgeois (University of Moncton) Part V: Innovating in Talent Attraction Chapter 12. What Does the Class Approach Add to the Study of Talent, Creativity, and Innovation in Canadian City-Regions? Bjorn T. Asheim (Lunds University)
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".