Documenting trauma in the age of transitional justice: A study of Jesse Hung Wai-Kin’s (J. C. Hung) documentaries on Taiwan’s Public Television Service
Bibliographic record
Abstract
This article examines the role of documentaries in addressing and healing trauma, as well as the pursuit of historical truthfulness during the age of transitional justice, with a focus on the documentaries of Jesse Hung Wai-Kin (JC Hung; 1950–2018) 洪維健 and its connection to Taiwan’s Public Television Service (PTS). As the first documentary director to concentrate his career on the White Terror period and transitional justice, Hung’s documentaries provide a case study to explore: (1) the impact of White Terror trauma on Hung’s filmmaking and screenwriting, particularly his narrative; (2) the transmission of personal traumatic experiences across generations and (3) the potential educational functions of these documentaries. Engaging with the concept of ‘postmemory’, this article investigates how trauma documentaries may facilitate intergenerational communication. Through a detailed textual analysis of Blind Night 暗夜哭聲 (2006), and ‘J. C. Hung’s study notes’ 洪維健的學習筆記 (2018), this study reveals his subjective, critical, and emotional style, emphasizing how his screenwriting contributes to shaping public perceptions of the historical trauma. Ultimately, this research contends that it is imperative to adopt a more inclusive perspective and respect the testimonies of victims.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".