“I Grew Up Longing to Be What I Wasn't”: Mixed-Methods Analysis of Amerasians' Experiences in the United States and Vietnam
Bibliographic record
Abstract
The Vietnam War left a legacy of mostly mixed-race children fathered by American (or other foreign) soldiers and born to Vietnamese mothers. These Vietnamese Amerasian children often had difficulties integrating into their post-conflict societies due to stigmatisation, and they were typically economically severely disadvantaged. This paper compares experiences of Amerasians in Vietnam with those who emigrated to the US as part of various departure programs since the end of the war in 1975. We used SenseMaker®, a mixed-methods data collection tool, to collect 377 narratives from 286 unique participants living in Vietnam and in the US exploring experiences of Amerasians in both countries. These narratives were then self-interpreted by the study participants using a questionnaire that generated a quantitative dataset. In this paper we analyse the self-coded perceptions quantitatively to determine patterns, specifically with view to investigating where experiences of Amerasians living in the US differ statistically from those living in Vietnam. This is complemented with a qualitative analysis of the accompanying narratives. Vietnamese respondents indicated more frequently that experiences were affected by economic circumstances than their US counterparts, and their identified negative experiences were significantly more strongly linked to poverty. Furthermore, Vietnamese respondents relayed that their desire to explore their biological roots was more prominent than US based participants, and they indicated more strongly than US counterparts that their biological parentage impacted their identity. In contrast, US respondents felt that their parentage impacted their physical and mental health in addition to impacting their identity, and they more strongly linked negative experiences in their narratives to their ethnicity.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".