Rose-Colored Genocide: Hollywood, Harmonizing Narratives, and the Cinematic Legacy of Anne Frank’s Diary in the United States
Bibliographic record
Abstract
Drawing from literary and cultural studies, this paper situates U.S. adaptations of Anne Frank’s diary in the 1950s within a lineage of other films about historical genocide, including Schindler’s List, Hotel Rwanda, and The Killing Fields. Analysis of these narrative adaptations matters because it helps us better understand the danger of what critic Dominick LaCapra calls “harmonizing narratives,” or stories that provide the viewer with an “unwarranted sense of spiritual uplift” (14). Tracing the metamorphosis of Frank’s own diary from play to film adaptation, this article builds on existing scholarship to focus on how, in the wake of what has become known as the Holocaust, Hollywood began to construct popular and simplified understandings of complex genocidal crimes—all in the name of celebrating globalized humanity. In the first part of the article, I take a longer view of these adaptations by situating U.S. interpretations of Frank’s diary within a lineage of other Hollywood versions of historical genocide, including The Killing Fields, Schindler’s List, and Hotel Rwanda. I argue that in making Anne Frank’s story morally simplifying and ultimately uplifting for U.S. audiences—in other words, shaping it into what critic Dominick LaCapra calls a “harmonizing narrative”—these Broadway and Hollywood adaptations privileged rose-colored narratology for that would influence future mainstream cinematic representations in dangerous ways. The second part of the paper then considers cinematic alternatives from outside of Hollywood (such as Canada, Rwanda, and Spain) that challenge these harmonizing narratives by enlisting a mise en abyme structure—in other words, the nesting of stories within stories—that ultimately suggest the full representation of genocide is impossible. By making false promises of harmony, Hollywood’s interpretation of Frank’s story has, in turn, limited our understanding of subsequent genocides. On the other hand, alternative modes of cinematic storytelling—most notably, ones such as Ararat that fracture a coherent narrative—compel the audience to grapple with questions of spectatorship, agency, and above all, the problems of representation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".