Interethnic contacts : a dynamic analysis of interaction between immigrants and natives in Western countries
Bibliographic record
Abstract
This book studies social integration of immigrants (i.e. contacts between immigrants and natives in leisure time) from a dynamic perspective. The central objective is to examine how such interethnic contacts change during the immigrants’ stay in the host country (do they increase, stagnate or decrease over time?), and to explain why some immigrants integrate socially at a faster pace than others (can the changes in interethnic contacts be explained by means of pre-migration and post-migration characteristics of immigrants?). An additional aim is to specify the conditions under which natives are more likely to engage in contact with immigrants. Throughout the book a theory of preferences, opportunities and third parties is used for deriving hypotheses about the determinants of interethnic contacts (e.g. education, language proficiency, age at migration). These hypotheses are then tested with data from three Western countries: the Netherlands, Germany and Canada. By applying a dynamic framework and relying on longitudinal surveys, more confident conclusions can be drawn about the causes of social integration. The main finding is that immigrants get increasingly socially integrated during the time spent in the host country. However, the pace of integration is on average rather slow. Immigrants who migrate at a younger age, as well as those who have a higher level of education and better language proficiency, tend to acquire more native friends over time. The same holds for immigrants inhabiting ethnically mixed, as opposed to segregated, neighborhoods. One of the strongest determinants of new interethnic contacts are the existing ties: immigrants get to know more native people especially if they already have some native friends and a native partner. While most of the characteristics that determine interethnic contact for immigrants also determine interethnic contact for natives, some of the characteristics have different effects for the two groups. For instance, higher educated immigrants have more contact with natives, whereas higher educated natives have less contact with immigrants. Such contrasting findings underline the relevance of studying immigrants and natives alike.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".