Highly-Skilled Migrants, Gender, and Well-Being in the Eindhoven Region. An Intersectional Analysis
Bibliographic record
Abstract
The shortage of skilled labor and the global competition for highly qualified employees has challenged Dutch companies to develop strategies to attract Highly Skilled Migrants (HSMs). This paper presents a study exploring how well-being is experienced by HSMs living in the Eindhoven region, a critical Dutch Tech Hub. Our population includes highly skilled women and men who moved to Eindhoven for work or to follow their partner trajectory. By analyzing data according to these four groups, we detect significant differences among HSMs. Given the exploratory nature of this work, we use a qualitative method based on semi-structured interviews. Our findings show that gender plays a crucial role in experienced well-being for almost every dimension analyzed. Using an intersectional approach, we challenge previous models of well-being, and we detect different factors that influence the respondents’ well-being when intersecting with gender. Those factors are migratory status, the reason to migrate, parenthood, and origin (EU/non-EU). When all the factors intersect, participants’ well-being decreases in several areas: career, financial satisfaction, subjective well-being, and social relationships. Significant gender differences are also found in migration strategies. Finally, we contribute to debates about skilled migration and well-being by including an intersectional perspective.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".