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Record W2058899492 · doi:10.1386/ejac.29.2.145_1

Multiple personality and the discourse of the multiple in Hollywood cinema

2010· article· en· W2058899492 on OpenAlexaff
Temenuga Trifonova

Bibliographic record

VenueEuropean Journal of American Culture · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsYork University
Fundersnot available
KeywordsHollywoodMovie theaterNarrativeAestheticsMental illnessTemporalitiesPersonalitySalience (neuroscience)AppropriationSketchSociologyPsychologyPsychoanalysisArtLiteratureEpistemologyMental healthPhilosophyPolitical sciencePsychotherapistLaw

Abstract

fetched live from OpenAlex

Hollywood has been instrumental in the de-pathologization of madness and mental illness through the appropriation of the symptomatic language of one particular mental illness, multiple personality, to create a new genre I call the multiple film: films dealing with multiple stolen, assumed or mistaken identities, realities or temporalities. Within the old illness model the multiple was the result of trauma; in these films, however, the multiple is both the result of trauma (or of another more mundane problem) and the solution to the trauma/problem. In this article I suggest some possible reasons for our current fascination with the multiple and for the depathologization of madness, sketch out the characteristics of the new genre of the multiple film, provide some representative examples of the genre and, finally, inquire into possible reasons for the Hollywood epidemic of the multiple. I argue that in an increasingly mediated culture, narratives involving multiple realities provide an outlet for the anxiety we feel over our passivity and powerlessness. They redeem the negative connotations of multiplicity instability, groundlessness and relativism by treating multiplicity as a reassuring surplus of possibilities.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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 designObservational
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

Citations2
Published2010
Admission routes1
Has abstractyes

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