Functional Creative Economies:The Spatial Distribution of Creative Workers
Bibliographic record
Abstract
Although cities face a myriad of challenges, they seem to be mitigated by the economic and agglomeration benefits that accrue to cities. Among these benefits is that high human capital and individuals disproportionately aggregate in cities. In fact, the share of the workforce that is highly-skilled increases with city size and not just the number of highly-skilled workers. This creates tremendous complications for smaller cities and rural areas that not only suffer drain but also have to address the economic, productivity, and prosperity challenges that result from having a lower share of the workforce in those occupations that generate those benefits. A possible source of remediation that has been offered is proximity to major agglomerations and metropolitan areas. Small cities and rural regions may be spatially advantaged by their proximity. Using detailed demographic and geographic data for Ontario from Statistics Canada, this paper investigates the relationship between population, density, proximity, and the share of the workforce in the creative class for all Ontario Census subdivisions (CSD). Population and density are always important factors for the local creative class. A linear spatial model revealed no significant relationship while a gravity model shows a minor but significant relationship. In general, only close proximity or a very large creative population is positively related to a larger creative class in small cities and rural areas. The results suggest that functional creative economies should be characterized by fairly limited spatial distances when considered on a provincial scale. Keywords: human capital, creative class, agglomeration benefits, brain drain, linear spatial model
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".