An Affinity for Wagner, Michelangelo, and Sadistic Torture: The Implicit Homosexuality of Captain Munsey in Jules Dassin's Brute Force
Bibliographic record
Abstract
Known for its transgressive narratives and anti-hero characters, film noir is an American film cycle that dominated the box office in the 1940's and 50's. At the time, homosexuality was banned from American cinema under the Production Code, yet film noir still managed to offer subtle and implicit representations of homosexuality. The sadistic prison guard in Jules Dassin's Brute Force (1947), Captain Munsey, is one such example and the film uses many signifiers to suggest his homosexuality. The implications that such a reading has on the narrative of the film are immense, and this paper merely scratches the surface of possible interpretations. Despite film noir's offering of some of the earliest portrayals of homosexuality in American cinema, scholarship on representations of homosexuality in film noir is few and far between. By revisiting Brute Force and examining how it manages to queercode Captain Munsey under the restraints of the Production Code, this paper seeks to spark new conversation among film scholars regarding homosexuality in film noir, and more broadly, films made under the Production Code. Moreover, the discourse surrounding representations of LGBTQ characters in American cinema has become increasingly mainstream, and by revisiting films from the past and analyzing them from a modern perspective, scholars can seek to gain new insight on the history of LGBTQ representation in cinema.
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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 | 0.000 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".