1 Innovation Systems Research Network The Social Dynamics of Economic Innovation Halifax City Region Study Theme 2: Social Foundations of Talent Attraction and Retention
Bibliographic record
Abstract
Recent thinking in economic theory presumes that healthy social environments in urban areas are crucial to positive economic performance. The work of Richard Florida (2002) and others has inspired a Canada-wide study (led by David Wolfe at the University of Toronto) documenting the relationship between different forms of social and civil engagement and economic growth. The study, Social Dynamics of Economic Performance, covers 15 cities of different sizes throughout Canada. Halifax, Nova Scotia is one of the medium-sized cities (250,000 – 999,999) in the study. Halifax Regional Municipality (HRM) has a population of 372,858 (2006 Census). Although most of the land area is rural, the largest proportion of the population lives in urban areas. The major economic drivers in HRM are government industries such as the Department of Defence, and institutions such as universities and health services. The research team for the Halifax study is in Dalhousie University’s School of Planning led by Jill Grant. This summary describes preliminary findings collected for theme 2 of the project, focusing on the Social Foundations of Talent Attraction and Retention. Based on the theory that the presence of creative people builds economic capital, the theme
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.007 |
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".