Talent, technology and tolerance in Canadian regional development
Bibliographic record
Abstract
This article examines the factors that shape economic development in Canadian regions. It employs path analysis and structural equation models to isolate the effects of technology, human capital and/or the creative class, universities, the diversity of service industries and openness to immigrants, minorities and gay and lesbian populations on regional income. It also examines the effects of several broad occupations groups—business and finance, management, science, arts and culture, education and health care—on regional income. The findings indicate that both human capital and the creative class have a direct effect on regional income. Openness and tolerance also have a significant effect on regional development in Canada. Openness towards the gay and lesbian population has a direct effect on both human capital and the creative class, while tolerance towards immigrants and visible minorities is directly associated with higher regional incomes. The university has a relatively weak effect on regional incomes and on technology as well. Management, business and finance and science occupations have a sizeable effect on regional income; arts and culture occupations have a significant effect on technology; health and education occupations have no effect on regional income .
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".