A Life Course Approach to High-skilled Migration: Lived Experiences of Indians in the Netherlands
Bibliographic record
Abstract
This article presents a framework which applies life course approach to high-skilled migration. By using the lens of the life course, migration behaviour is viewed not only in response to labour market triggers, but also in relation to other life domains such as education, employment and household. The data presented in this article are drawn from 22 in-depth interviews and visualisations of parallel careers. The results illustrate how highly skilled Indian migrants in the Netherlands shape their life course and highlight the parallel careers that structure their migration trajectories. Parents, spouse and social networks inform the life course decisions of these migrants through the linked lives mechanism to a large extent. Our findings challenge the notion of ‘trailing wives’ and suggest that, despite of gender differences in the life course patterns, the joining spouses play an active role in the family migration decisions of the highly skilled. Life course approach enables us to understand the migration process through the lives of the highly skilled and reveals how—the often culturally conditioned—life course interdependencies frame their migration decisions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".