The dead and the abhorred: <i>Mindhunter</i> and the persistence of mother-blame
Bibliographic record
Abstract
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".