“American Goddess of Mercy” Revisited: Horror Behind the Mundane Details in Ha Jin’s Nanjing Requiem
Bibliographic record
Abstract
This essay examines how Ha Jin deploys historical materials such as diary and letters as a means of structuring a transnational Nanjing Massacre narrative. Particularly, I address Ha Jin’s “critical historical consciousness” in representing the Nanjing Massacre, which is first and foremost, closely related to his intellectual dilemma as a migrant writer— whether to use English to write stories about China in the U.S. Then I argue that in adapting Vautrin’s diary, Jin emphasizes exhaustive, details that give us a channel to mediate the loaded term of “American Goddess of Mercy.” Furthermore, through these mundane details, Jin portrays a Maussean notion of gift economy in Vautrin’s management of Ginling College, as Jin directs attention towards the horror of “complicity” between Vautrin and the Japanese army. In addition, he attempts to represent the unrepresentable—the failure of such a gift economy—exemplified by cruel rapes committed by Japanese soldiers inside the camp who consider Vautrin as their “friend.” I argue that Ha Jin calls attention to the pitfall of traditional historiography and demands us to re-examine the usage of historical materials in aesthetic works.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".