Relational Bonds as Method: Oral History and Design within the Vietnamese Diaspora
Bibliographic record
Abstract
Despite the Vietnam War becoming one of the most documented and publicized conflicts in recent history, nuanced discussion and intersectional historical data of the Vietnamese diaspora remains limited. Invisibility and erasure of this community has contributed to reductive and racist perspectives on the war and ongoing intergenerational trauma. Current archives prioritize American viewpoints while minimizing and often dehumanizing Vietnamese perspectives. Historical archives on this conflict need to include more Vietnamese diasporic voices, not only to gain accuracy and insight into the past, but to also allow people of Vietnamese descent to process and heal generations of trauma caused by continuous imperialist occupation and civil war. The complex tasks of remembrance, decolonization, and possible Vietnamese reunification will become even more challenging once first-hand experiences are no longer available and survivors of The Vietnam War pass away. \nThe primary research question for this project explores how storytelling and oral history can decolonize, deconstruct and challenge typical archetypes, narratives and taxonomy related to Vietnamese refugees found in historical archives and current media resources. Secondly, this research work strives to understand how future generations of the Vietnamese community can move towards understanding multiple political and social perspectives of conflict through oral histories. Thirdly, the latter part of this project explores how time and relationship building influences research and design, with a focus on the Vietnamese diaspora in the Greater Toronto Area.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".