What’s Up, Tiger Lily? On Woody Allen and the Screen Translator’s Trojan Horse1
Bibliographic record
Abstract
Woody Allen made his transition from stand-up comedy to cinema not as an author, but as a dialogue adaptor and film dubber. In 1966, he transformed a Japanese spy thriller into an American comedy by removing the film’s original dialogue and soundtracks, and then synchronizing a new dialogue of his own penning with the original film’s images. The result wasWhat’s Up, Tiger Lily?(1966), a film where Allen forces a cast of unwitting Japanese characters to act out one narrative visibly as they speak out another audibly. The film suggests a number of intriguing theoretical vectors for those interested in the subject of screen translation as a mode of intercultural appropriation (or misappropriation).What’s Up, Tiger Lily?,first of all, is a comedic exploration of authorial status in cinema. Indeed, the lesser status of “re-writer” becomes Allen’s cover, a way to avoid taking responsibility for a film that not only indulges in the most counterintuitive of experiments in the sound-image relationship, but also creates a particularly condescending form of Asian exploitation. Perhaps most important, however, is the perspective that the film offers on the voice-image antagonism implicit in any foreign-language dubbed film. Allen’s film may well offer a way for theory to transcend the aura of negativity with which academic discourse tends to surround the practice of dubbing, specifically by putting the latter to use in the service of intercultural parody. Michael Cronin’s latest work on globalization and Hollywood (2009) offers some helpful concepts for examining Allen’s film.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".