Bibliographic record
Abstract
De welvaartswinsten of –verliezen verbonden aan immigratie zijn verwaarloosbaar klein voor de Nederlandse economie. Nettowinsten van immigratie verbergen evenwel een forse herverdeling van inkomens tussen hoogen laaggeschoolde werknemers en kapitaal. De achilleshiel van Nederland is echter de verzorgingsstaat. De gedachte dat met immigratiepolitiek binnenlandse economische problemen zoals vergrijzing en arbeidstekorten kunnen worden opgelost is meestal kortzichtig en gaat voorbij aan structurele tekortkomingen van de binnenlandse economie. In tegenstelling tot traditionele immigratielanden als de VS, Canada en Australie zal Nederland als immigratieland, gezien de omvang van de verzorgingsstaat, veel meer aandacht aan het handhaven van solidariteit moeten besteden. Trefwoorden: immigratie; economie; Nederland. Immigration: A curse or a blessing for the Dutch economy? The net welfare gains or losses connected with immigration are extremely small for the Dutch economy. Net immigration gains do however cover up a quite substantial redistribution of income between highly skilled and low skilled and owners of physical capital. The weak spot of the Netherlands, like many other European countries, is its welfare state. The thought * De auteur is als senior onderzoeker verbonden aan het Onderzoekcentrum Financieel Economisch Beleid (OCFEB) van de Erasmus Universiteit Rotterdam, fellow van het Tinbergen Instituut en stafmedewerker van de WRR. Dank gaat uit naar het waardevolle commentaar van een anonieme referent.
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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 | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".