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Record W7084758488 · doi:10.18573/ipics.144

'You're a Triple Imposter, I've Never Seen Anything Like It': Scarlett Johansson's Femme Fatales

2025· article· en· W7084758488 on OpenAlexfundno aff

Bibliographic record

VenueIntersectional Perspectives Identity Culture and Society · 2025
Typearticle
Languageen
FieldMedicine
TopicLymphadenopathy Diagnosis and Analysis
Canadian institutionsnot available
FundersUniversity of Northern British Columbia
KeywordsCharacter (mathematics)Identity (music)Star (game theory)Movie theaterPerformance art

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.298
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueIntersectional Perspectives Identity Culture and SocietySame topicLymphadenopathy Diagnosis and AnalysisFrench-language works237,207