Highly-Skilled Migrants, Gender, and Well-Being in the Eindhoven Region. An Intersectional Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it