The Dutch battle for highly skilled migrants: policy, implementation and the role of social networks
Bibliographic record
Abstract
In recent years a growing competition for talent has emerged among developed nations. Policymakers across North-America, Australia and Europe have implemented targeted migration programs to attract global talent in order to gain the net positive effects associated with skilled migration. Research so far has mainly focused on analyzing such programs in traditional destinations for highly skilled migrants such as the United States, Canada and Australia. In this article we take the Netherlands as a case study of the more recent European involvement in the ‘race for talent’. We first describe how ‘highly skilled’ migrants are categorized in the various skilled migration schemes that exist in the Netherlands. Secondly, by using primary data on highly-skilled migrants who participated in one of these schemes we look at whether the policy measures attracted the intended target group. We conclude that policy measures that favor highly skilled migrants by themselves are not enough to attract talent. Having social capital in the Netherlands as well as the recruiting efforts of Dutch employers are more important in attracting highly skilled migrants. Also, being highly skilled does not necessarily mean that access to the Dutch labor market is without obstacles.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".