Curating the Ama museum for Taiwanese comfort women: a humanistic approach to traumatic memory and healing
Bibliographic record
Abstract
As survivors of the Japanese military’s sexual slavery system (‘comfort women’) have rapidly passed away, museums preserving their memories have been key for justice, advocacy, and memorialization. Taiwan’s Ama Museum, established in Taipei in 2016, was dedicated to commemorating Taiwanese comfort women. This study documents the museum’s curatorial approach between 2016 and 2018 at its original Dadaocheng site, focusing on how it embodied a humanistic discourse to aesthetically curate survivors’ traumatic history and life resilience through architecture, exhibition design, and educational programming. We then draw on family and public perspectives to engage in collaborative storytelling about the museum’s humanistic approach. Interviews with five family members of comfort women survivors revealed that the museum’s content, approach, and legacy resonated deeply. Participants articulated that the museum offered inspiration and the potential for resilience, creating hope and strength. Yet its eventual closure in 2020 and relocation evoked deep sadness and regret. Additionally, 22 reviews of a travel website were analysed as triangulated responses to the Ama Museum and its overall impact. The findings highlight the marginalization of the issue and waning public support as essential challenges to operating a comfort women’s museum in Taiwan, requiring greater support to safeguard and preserve survivors’ legacy.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.009 |
| 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".