1 Innovation Systems Research Network The Social Dynamics of Economic Innovation Halifax City Region Study Theme 2: Social Foundations of Talent Attraction and Retention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it