Cities and Growth: The Left Brain of North American Cities: Scientists and Engineers and Urban Growth
Bibliographic record
Abstract
This paper examines the growth of human capital in Canadian and U.S. cities. Using pooled Census of Population data for 242 urban centres, we evaluate the link between long run employment growth and the supply of different types of skilled labour. The paper also examines whether the scientific capabilities of cities are influenced by amenities such as the size of the local cultural sector. The first part of the paper investigates the contribution of broad and specialized forms of human capital to long-run employment growth. We differentiate between employed degree holders (a general measure of human capital) and degree holders employed in science and cultural occupations (specific measures of human capital). Our growth models investigate long-run changes in urban employment from 1980 to 2000, and control for other factors that have been posited to influence the growth of cities. These include estimates of the amenities that proxy differences in the attractiveness of urban areas. The second part of the paper focuses specifically on a particular type of human capital'degree holders in science and engineering occupations. Our models evaluate the factors associated with the medium- and long-run growth of these occupations. Particular attention is placed on disentangling the relationships between science and engineering growth and other forms of human capital.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".