ATTRACTING AND RETAINING ACADEMIC TALENT IN THE CITY OF KINGSTON, ONTARIO
Bibliographic record
Abstract
Recent analyses of creativity in the North American economy have underscored the importance of city-regions in the generation of economic dynamism.These studies have been concerned with at least two principal assertions.The first assertion is that the social dynamics of city-regions constitute the foundations of economic success.The second assertion is that the distribution of human capital (talent) is a crucial element in regional economic prosperity; yet the distribution of human capital across cities is uneven.Therefore, the question emerges: what factors influence the locational choices of talented individuals?In recent years, this question has received considerable scholarly attention.This thesis has identified two existing gaps within this field of inquiry.Conspicuously absent from studies in this area are theoretical insights offered by cultural geographers in the field of whiteness and race.Economic geographers have created an essentialized reading of racial diversity in the economic performance of city-regions.Moreover, work in this area has been constrained by a quantitative focus and a lack of empirical evidence.Accordingly, the purpose of this thesis is to develop a more nuanced understanding of how social processes and institutions underlie and are shaped by the economic performance of city-regions.This is achieved by drawing on insights from an empirical study of 44 semi-structured interviews with academic talent in the City of Kingston, Ontario and 12 interviews with community insiders.The results on the one hand reveal complex dynamics linked to why academics live in particular places, but on the other hand point to one overriding explanation for why academics locate where they do: namely, academics are attracted to Kingston, first and foremost, because of academic jobs, not urban amenities or other characteristics of place.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".