Life courses of Amerasians in Vietnam: a qualitative analysis of emotional wellbeing
Bibliographic record
Abstract
The Vietnam War left a legacy of mixed-race children fathered by American or other foreign soldiers and born to Vietnamese mothers. These Vietnamese Amerasian children often had difficulty integrating into their post-conflict societies due to stigmatization and they were typically economically disadvantaged. To address the paucity of knowledge about life courses of Amerasians who remained in Vietnam, we used SenseMaker®, a mixed methods data collection tool, to interview adult Amerasians living in Vietnam. Qualitative analysis of first-person narratives categorized by participants as being about “emotions” identified five major themes: discrimination, poverty, identity, the importance of family, and varying perceptions of circumstances. Experiences of discrimination were broad and sometimes systemic, affecting family life, the pursuit of education, and employment opportunities. Poverty was also an overarching theme and was perceived as a barrier to a better life, as a source of misery, and as a source of disempowerment. The resulting cycle of poverty, in which under-educated, resource-constrained Amerasians struggled to educate their own children, was evident. The negative emotional impact of not knowing one’s biological roots was also significant. Although there was a decrease in perceived stigma over time and some Amerasians were satisfied with their current lives, years of experiencing discrimination undoubtedly negatively impacted emotional well-being. The results highlight a need for community programs to address stigmatization and discrimination and call for support in facilitating international searches for the biological fathers of Vietnamese Amerasians.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".