Untangling Space and Career Action: Migrant Career Recontextualization in the Host City
Bibliographic record
Abstract
As many skilled migrants settle in global cities, we explore how physical and social embeddedness in host cities may predispose migrant career action and integration. We highlight the significance of migrants’ desire for spatial continuity and belongingness as the foundation for their career efforts. In crossing city boundaries, migrants interact and learn from host city artifacts; thus, we illustrate the facilitating and constraining role of the host city on migrants’ ability to apply and translate their foreign career capital locally. We discover career recontextualization that embodies not only transfer, but also translation and transformation, of career knowledge from home to host city context, through local boundary objects (e.g., city artifacts) as intermediaries. Career recontextualization is enacted via three unique types of career action: career orienting, cross-boundary career adaptation, and creative career action (e.g., new boundary object creation). Thus, we extend boundary object theory to the city context and explore the role of transferring work-related knowledge as well as the ability to control and influence careers of newcomers. Finally, we provide a novel perspective on the intricate relationship between career recontextualization and migrant integration in the host city, leading to a discovery of two unique types of integration (functional and holistic).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".