The bi-directional impact of a mixed union. People without a migration background in a union with a partner with a migration background
Bibliographic record
Abstract
Of the BaM respondents in a relation, no less than a quarter is in a mixed union. We still know very little about the non-migrant partner in unions with a partner with a migration background, including their propensity to adopt the cultural practices of their partner, and their propensity to reach out and embrace ethnic diversity more generally. The growth of intimate relationships between people with and without a migration background in majority minority cities in Europe provides an opportunity to explore the attitudes and experiences of non-migrant individuals in interethnic unions, and what such unions may portend for the wider society. This article makes a critical contribution to the general debate on the assimilation paradigm, which predicts ‘a whitening’ of norms and practices in mixed unions. We will use the BaM data to investigate the potential bi-directional effect of being in a mixed union. Does a mixed union, as assimilation scholars argue, primarily have a whitening impact on the minority partner, or is there also a potential diversifying impact upon the other partner?
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".