Never Tell a Psychopath They’re a Psychopath: Defamiliarizing the Queer Psychopath in Killing Eve
Bibliographic record
Abstract
“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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.048 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".