Othering mechanisms and multiple positionings: Children of Thai-Belgian couples as viewed in Thailand and Belgium
Bibliographic record
Abstract
Studies on the so-called “second generation” mainly focus on individuals whose parents are both migrants. This overlooks the situation of the children of “mixed” couples, in which one parent is a migrant and the other a citizen of the country in which they live. These mixed-parentage young people mostly inhabit cross-border social spaces that connect their parents’ respective countries of origin. Given this situation, how are these young people viewed in the countries in which they are enmeshed? How do they position themselves in relation to the viewpoints and stereotypes about them in these social spaces that traverse the borders of nation-states? To answer these questions, the present study examines the case of children of Thai-Belgian couples, who are called luk-kreung (half-child) in Thailand and métis in Belgium. Analysis of the empirical data gathered using qualitative methods shows that the study informants had access to citizenship in both of their parents’ countries of origin. Nonetheless, they remained widely subjected to othering due to their phenotypic characteristics, which are perceived as “different” from those of the majority population. This othering prompted them to adopt multiple positioning strategies: invisibilising their ethnic roots, accepting and highlighting their supposed “Otherness”, and acquiring Thai nationality (for those who did not yet have it).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".