'You're a Triple Imposter, I've Never Seen Anything Like It': Scarlett Johansson's Femme Fatales
Bibliographic record
Abstract
Researchers investigating Scarlett Johansson’s films and image have neglected an essential strand of the star’s career. Scholars have mostly demonstrated no interest in Johansson’s film noirs. In this article, I offer one step forward in correcting this oversight. Johansson’s four noir roles point to a larger tendency in her film output: the noirs firmly position Johansson as an A-list actress with a specific type of star vehicle. In her noirs and many of her films in other genres, Johansson plays a role in which the character must also play a role, wear a mask, or hide her identity (or identities). I name this type of role performing performing. Film noir is well suited for this type. The femme fatale often hides her agenda, identity, or motive behind the feminine masquerade. With the assistance of star studies theories and Mary Ann Doane’s observations on the feminine masquerade in twentieth century cinema, this article turns to Johansson’s four noir roles and offers two theses. First, while Johansson acts across genres and styles, she nevertheless possesses a specific trait, performing performing, that cuts across her work and thus establishes her films as star vehicles. Second, analysing her performances in film noirs demonstrates how her twenty-first century films diverge from Doane’s accounts of the twentieth century’s femme fatales. This article intervenes in ongoing debates about gender representation, stardom, and performance in contemporary 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".