Creative Talent in Relation to the City: The Case of a Natural Resource-Based Centre (Calgary)
Bibliographic record
Abstract
A large recent literature argues that cities’ capacity to attract and retain creative talent crucially supports innovation and economic health. Instead of understanding ‘creative’ talent contributions statistically through education, job classification, income, and economic growth, this paper qualitatively explores creative workers’ attitudes about the city in which they pursue a career. This paper reports on 28 factors of attraction and retention of creative talent in Calgary, a natural resource-based centre in Canada studied in the years 2006–2008. The data were drawn from interviewees’ responses to questions about attitudes toward the city as a place to work and about possible moves to alternative locations, in the context of a study of the social dynamics of innovation from the city perspective. The qualitative expressed preference methodology reveals the complexity of factors shaping individual preference for place, exposing a richness not accessible through regression analysis on statistical categories alone. Identification of 28 ‘embeddedness’ factors expressed in the interviews facilitates a grounded theory classification under seven main aspects of the socio and economic infrastructure that could be used to construct and test an indicator of the relation to the city. Given adequate economic opportunities, we find that several environmental factors, personal networks and professional networks were most attractive, while socio-cultural diversity was less emphasized. A multi-dimensional analysis could explain Calgary’s attraction of internal migration beyond growth predicted by population size and the characteristically dynamic growth of larger centers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.010 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".