Alternative career pathways of skilled migrants: looking for new meanings amid starting again
Bibliographic record
Abstract
This qualitative study aims to provide an in-depth understanding of skilled migrants’ lived experiences of alternative careers. We explore identity work and meaning-making processes of career actors for whom alternative career options often meant “beginning again”. While focusing on psychological, temporal, and contextual dimensions of alternative career transitions, our findings identify three unique alternative career pathways: provisional, experimental, and reformist; each characterized by a unique form of identity work and accompanying types of meaning-making. We find that alternative career pathways differ in terms of their temporary and at times provisional nature as well as career actors’ (in)ability to engage in the present search for meanings and purpose in alternative careers. This study advances existing literature on major career transitions and specifically migrant career trajectories inside of local organizations and through unique forms of alternative careers. We also build on the existing meaning-making literature by highlighting the career narratives of those who must search for new meanings while pursuing “less than ideal” career opportunities. Finally, our findings provide practical implications related to outcomes of alternative career opportunities on migrant career success but also more broadly for employers and policymakers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".