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Record W3182982590 · doi:10.1177/17416590211031282

The dead and the abhorred: <i>Mindhunter</i> and the persistence of mother-blame

2021· article· en· W3182982590 on OpenAlexaff
Michele Byers, Rachael Collins

Bibliographic record

VenueCrime Media Culture An International Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsBlameAntithesisMasculinityHumanityNarrativeMainstreamMotif (music)PsychoanalysisSociologyPsychologySocial psychologyGender studiesAestheticsLiteratureArtPhilosophy

Abstract

fetched live from OpenAlex

In her study of violent protagonists in American literature, Wilson-Scott argues that “mothers are frequently used as the principle traumatizing factor, demonized and depersonalized in order to reassert their violent offspring’s humanity” (p. 191). Further, Wilson-Scott states that her work “reveals the persistent assumption that mothers make monsters” (p. 193). Taking our tacit agreement with Wilson-Scott as a starting point, we argue along with her that mother-blame remains a central motif of mainstream cultural narratives about violent masculinity. The focus of this essay is on the strategies through which mother-blame is used to validate the authorial authenticity of the male serial killer and his ways of knowing and of being in the world. In this essay we offer the first season of the popular Netflix series Mindhunter (2017–) as a case study and ask how the representation of the serial killer’s insight and seemingly accurate understanding of his own pathology is linked to its antithesis, woman-hate, and often, the pathologizing of the mother.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.627
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.018
GPT teacher head0.295
Teacher spread0.278 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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