Self-interpreted narrative capture: A research project to examine life courses of Amerasians in Vietnam and the United States
Bibliographic record
Abstract
When American troops withdrew from Vietnam in April 1975, they left behind a large number of children fathered by American GIs and born to local Vietnamese women. Although there is some documentation of experiences of GI children who immigrated to the United States, little is known about the life courses of Amerasian children who remained in Vietnam, and no comparative data has been collected. To address this knowledge gap, we used an innovative mixed qualitative – quantitative data collection tool, Cognitive Edge’s SenseMaker®, to investigate the life experiences of three specific cohorts of GI-fathered children from the Vietnam War: (1) those who remained in Vietnam, (2) those who immigrated to the United States as babies or very young children and (3) those who immigrated to the United States as adolescents or adults. The current analysis reflects on the implementation of this mixed-methods narrative data collection and self-interpretation tool as a research methodology in Vietnam and the United States and outlines some of the challenges and lessons learned including recruitment of a hard to reach population, low response rates in the United States and feasibility of using such narrative capture to conduct such research in the United States and in Vietnam.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".