Val Lewton and the Grand-Guignol: Mademoiselle Fifi and horror canonicity
Bibliographic record
Abstract
Abstract This article explores a film produced by Val Lewton that is not usually included within his horror canon, Mademoiselle Fifi (Wise, 1944), as embodying important elements of the tradition of the Grand-Guignol theatre. The Grand-Guignol was the infamous Paris theatre (1897–1962) popularly associated with excessive onstage blood-letting, vitriol burns and dismemberment. I argue that the Grand-Guignol and Lewton’s film have much more in common than is often considered by scholars of the horror genre. Because of the film’s explicitness with respect to its politics and use of violence, even under the Production Code, Mademoiselle Fifi challenges the myth of Lewton, as a man of the shadows, of restraint and the indirect. I argue that the terms ‘Lewtonesque’ and ‘grand-guignolesque’ are not mutually exclusive, dislodging the reductive dichotomy (terror/horror) about the Lewton canon and the horror genre more broadly. Moreover, I consider the Grand-Guignol stage in ways that move beyond its popular misrepresentation as solely a place of excessive bloodletting by retrieving such techniques as ‘signposting’ and the ‘moment of violence’, usually associated with the experience of hidden terror, not only visible horror. This article locates Val Lewton’s work within a more complex set of intertextual convergences in order to broaden narrow understandings of horror canonicity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".