Bibliographic record
Abstract
Despite the increasing prevalence of mixed-race couples and their children in Denmark, the phenomenon of interracial relationships is overlooked, evident through the limited academic research, statistics, and psychosocial services. These couples’ voices are largely unheard in the Nordic context, which is often characterized by Nordic exceptionalism, homogeneity, historical silencing of mixedness, and a colorblind ideology. This chapter addresses this oversight, giving voice to these interracial couples by highlighting their experiences. It starts by delineating the Danish/Nordic conflation of race/ethnicity, presenting statistics about interracial couples, and discussing three interracial relationship philosophies: overlooking , exoticizing , and c elebrating . Later it delves into relevant Danish studies along with some Nordic, British, American, and Canadian. The chapter covers the couples’ lived experiences, including their responses to discriminatory experiences, primarily based on two empirical studies. The studies conduducted by the author in 2024 and 2015), have a decolonial theoretical framework that combines a cultural-psychological perspective with an intersectional approach, as well as the concepts of “race work”andthe “shared third”’ Lastly, the chapter presents the implications for mixed-race couples’ mental health and well-being, a crucial aspect entailing immediate attention. The conclusions emphasize raising awareness and addressing the phenomenon of mixed relations in psychosocial services, policy, media, and research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".