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Record W4411985732 · doi:10.15353/cjds.v12i3.1034

Never Tell a Psychopath They’re a Psychopath: Defamiliarizing the Queer Psychopath in Killing Eve

2023· article· en· W4411985732 on OpenAlexvenueno aff
Clare Sears

Bibliographic record

VenueCanadian Journal of Disability Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsQueerPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

“Psychopath” is a highly contested culturally constructed category of knowledge that has deep and durable ties to queerness. Multiple scholars have explored these ties in psychiatric and legal discourse, as well as in popular media that frames psychopathy as synonymous with monstrosity. Few studies, however, have explored media representations of the queer psychopath as a distinctly psychiatric type. To address this gap, this article explores representations of the queer psychopath figure in BBC America’s hit television show Killing Eve (2018-2022). Rooted in historical and cultural analysis, the article documents close linkages between psychopathy and queerness during the twentieth century and explores Killing Eve’s recent engagements with the queer psychopath trope. Using the concept of defamiliarization, the article argues that Killing Eve disrupts queer psychopathy as a category of knowledge in three specific ways: (a) multiplying and dispersing non-normative sexualities and psychologies across numerous settings, (b) deploying same-sex desires as a mechanism for psychopathy’s undoing, and (c) destabilizing psychiatric authority and expertise. The article considers key inconsistencies within the show and concludes that Killing Eve actively – if unevenly – facilitates a reimagination of non-normative emotions, psychologies, and desires.

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)0.005
metaresearch head score (Gemma)0.002
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 score0.642
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.060
GPT teacher head0.356
Teacher spread0.296 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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