Narrativizing trauma, activating awareness: Iris Chang’s<i>The Rape of Nanking</i>and its afterlives
Bibliographic record
Abstract
When Iris Chang published The Rape of Nanking in 1997, exactly sixty years after the Nanjing Massacre, the subtitle The Forgotten Holocaust of World War II, called attention to one of the greatest human tragedies in the twentieth century. As a powerful historic reminder, The Rape of Nanking aims “to understand the event so that lessons can be learned and warnings sounded.” This paper focuses on Chang’s role as a writer/fighter who uses words to fight forgetfulness with a forceful narrative concerning one of the most dreadful traumas in the collective psyche of the Chinese people. It produces quite a number of “afterlives,” including different Chinese translations in Taiwan and mainland China, a nanking winter (2008), a play by the second-generation Chinese Canadian playwright Marjorie Chan, Nanjing Requiem (2011), a novel by the first-generation Chinese American novelist Ha Jin, and The Nanjing Massacre: Poems (2013), a collection of poems by the third-generation Chinese Hawaiian poet Wing Tek Lum. Furthermore, the docudrama Iris Chang: The Rape of Nanking (2007), directed by Bill Spahic and Anne Pick, presents a filmic representation of the short fascinating life of this passionate writer. This paper discusses how Chang, role as a writer and activist, fights against amnesia with remembrance as well as her rich legacy to the world across linguistic, generic, and semiotic boundaries. Chang’s text and its afterlives strive to give voice to those nameless war victims as a step towards truth, justice, reconciliation, and peace.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".