Crossing national boundaries: A typology of qualified immigrants' career orientations
Bibliographic record
Abstract
Abstract This qualitative study examines objective–subjective career interdependencies within a sample of 45 qualified immigrants (QIs) in Canada, Spain and France. The particular challenges in this type of self‐initiated international careers arise from the power of institutions and local gatekeepers, the lack of recognition for QIs' foreign career capital, and the need for proactivity. Resulting from primary data analysis, we identify six major themes in QIs' subjective interpretations of objective barriers: Maintaining motivation, managing identity, developing new credentials, developing local know‐how, building a new social network and evaluating career success. Secondary data analysis distinguishes three QI career orientations—embracing, adaptive and resisting orientations—with each portraying distinct patterns of motivation, identity and coping. This study extends the boundaryless career perspective by providing a more fine‐grained understanding of how qualified migrants manage both physical and psychological mobility during self‐initiated international career transitions. With regards to the interdependence between objective and subjective career aspects, it illustrates the importance of avoiding preference to one side at the neglect of the other, or treating the two sides as independent of one another. Practical implications are proposed for career management efforts and receiving economies. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".