Bibliographic record
Abstract
monolithic project feels worryingly accidental.The successful completion of the project on the other hand has been anything but random, and instead was made possible by the tireless efforts and support of family, friends and colleagues.In these acknowledgments, I will try to do them justice.First and foremost, heartfelt gratitude goes to my supervisors, Maarten Vink and Hans Schmeets.This book owes its existence to their extraordinary academic insight, palpable enthusiasm for the job, and deep sense of care and commitment.Maarten and Hans: you are a truly inspirational team, and your attention for detail and ability to provide instant, high-quality feedback are legendary.As Bismarck apparently never said: "(…) the less you know about the process, the more you respect the result".He was talking about sausages and law-making, but it applies to PhD's as well.As supervisors, you were privy to all the bumpy ins and outs along the way, but I could always rely on your enduring patience and understanding when it occasionally all went pear-shaped.I am honoured to have been under your supervision, and I am very glad that I have been given the opportunity to continue working with you both in the coming years.I also want to take this opportunity to express my gratitude to the assessment committee, with special mention to Pieter Bevelander, who generously agreed to host me in Malmö for research as I am writing this.A substantial part of my PhD was spent at CBS, trying to get all my proverbial dataducks in a line, and grappling with unfamiliar analytical software.Without the monumental support, patience and understanding of my colleagues there, none of the analyses would have been possible.Special thanks goes to Koos Arts, Clemens Siermann and Henk Florie (and any other data-specialists working on my applications behind the scenes), who graciously and repeatedly took the time to provide me with the vast and complex data that I needed.I am also grateful to Marly Odekerken and the SocSamteam for hosting me (and those who followed in my footsteps), and generally making me feel very welcome.It has been a pleasure, and I am happy that I can stay a while longer in my capacity as postdoctoral researcher.My time as a PhD has been highly enjoyable, in no small part because of my colleagues at Maastricht University.First, sincere gratitude goes to Marloes de Hoon, whose arrival at the faculty heralded a number of uncoincidental breakthroughs in my PhD.Marloes: your kindness, insight and openness have been instrumental in my ability to navigate the project into the smooth sailing waters they have generally been in.Your mark is indelibly etched on many aspects of the dissertation.Support was further cemented by the new MiLifeStatus arrivals: Swantje Falcke, Marie Labussière and advantages of the Dutch case and the use of administrative data in the context of this dissertation are discussed below.More generally, the aim of the thesis is to answer the following central research question: what are the determinants of citizenship acquisition, and what is the relevance of naturalisation for the socio-economic integration of first generation immigrants in the Netherlands?From a societal perspective, this thesis aims to contribute to effective, targeted policy making in the field of naturalisation and socio-economic integration of immigrants.Although there is a large field of literature that has analysed the effects of citizenship in the labour market (e.g.OECD, 2011), these studies focus almost exclusively on the question whether citizenship matters or not.Such research is of limited use for policy makers, because there is substantial heterogeneity in citizenship regimes and pathways to citizenship.For example, migrants may acquire citizenship early or late in the settlement process, and at different stages of their life course.Citizenship policies differ between countries and over time, and may not matter equally to all migrant groups.This thesis specifically analyses to whom and under which conditions citizenship matters, providing policy makers with a more detailed understanding of the outcomes of citizenship policies for particular migrant groups, and offering evidence-based suggestions on how to facilitate settlement success of immigrants. A life course approach to naturalisation and socio-economic integration of immigrantsResearch on the labour market outcomes of naturalisation is an established field of literature that dates back to the late seventies, and originated in the United States (Chiswick, 1978).In light of the structural disadvantages of immigrants in the labour market (Algan, Dustmann, Glitz, & Manning, 2010), scholars have theorized that citizenship may facilitate integration by removing legal obstacles to labour market access and reducing administrative costs in the hiring process (Bauböck et al., 2013; OECD, 2011).Furthermore, employers may assume that possession of the host country citizenship reflects positive characteristics such as commitment and motivation.Citizenship may thus function as a signalling device that placates feelings of risk associated with hiring a foreign-born individual.More recently, North-American studies have been replicated in the European context (e.g.Bevelander & Veenman, 2008;Bratsberg & Raaum, 2011;Fougère & Safi, 2009;Helgertz et al., 2014;Steinhardt, 2012).Although the analytical models have developed over time, almost all of these studies share the same underlying goal: to identify whether a citizenship premium exists or not.Yet after numerous studies across a range of countries, this remains an open question.Citizenship acquisition confers individual rights and lifts legal obstacles to participation, but it is not so clear whether naturalisation also stimulates socioeconomic integration.Empirical findings paint an ambiguous picture that frequently, yet Outline of the dissertationThe thesis is structured along five substantive chapters based on a combination of published and submitted research articles.Chapter 2 introduces the theoretical innovation of the thesis.First, I outline the state-of-the-art literature on the citizenship premium, and identify the need for a more developed theoretical framework that may 7 See Bevelander and Helgertz (2016) and Street (2014) for a quantitative and qualitative exception respectively.
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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 | 0.007 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".