Careers in Cities: Improving Lives, Improving Communities
Bibliographic record
Abstract
In this symposium, we present four examples of ways in which an exploration of interactions between careers and cities can enhance our understanding of both individual and institutional dynamics relevant to work and wellbeing. Setor, Joseph, and Chan present an empirical study which illustrates a technique for distinguishing career patterns, their differing prevalence in city and rural contexts, and the effect of these differences on career outcomes. Gill presents results of a longitudinal study of the emergence of a new innovation ecosystem, reflecting on tensions between economic priorities and acknowledgement of local place, culture, and identity in supporting entrepreneurs. Feltner, Pandzich, and Mitra explore ways in which entrepreneurs and a broader entrepreneurial ecosystem can contribute to individual and community wellbeing and urban renewal. Zikic and Voloshyna apply intelligent career theory to the transition faced by migrants as accumulated career capital is disrupted, and ways in which new host cities influence career reestablishment. Are the Career Patterns of City, Suburban and Rural Dwellers Different or Similar? Presenter: Tenace Kwaku Setor; U. Of Nebraska Omaha Presenter: Damien Joseph; Nanyang Technological U. Presenter: Kim Yin Chan; Nanyang Technological U. Understanding Discursive Agendas in City Efforts to Build Innovation Ecosystems Presenter: Rebecca Gill; Wake Forest U. Entrepreneurial Careers for Urban Resilience in Legacy Cities: Narratives from Detroit Presenter: Elizabeth-Ann Pandzich; Wayne State U. Presenter: Rahul Mitra; Wayne State U. Presenter: Dorothy Feltner; Wayne State U. New City, Foreign Career Competencies: Crafting New Pathways to Migrant Career Success? Presenter: Jelena Zikic; York U. Presenter: Viktoriya Voloshyna; York U., Canada
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.024 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".