Bad Blood: Serial Killers, True Crime, and the Racial Imaginary in <i>Shadow of a Doubt</i>
Bibliographic record
Abstract
Concepts of racial identity and parentage played an important role in Canadian media coverage of the 1927 case of Earle Nelson, a well-publicized true crime story that was adapted into Hitchcock’s 1943 film Shadow of a Doubt. Nelson, known as the Dark Strangler, was identified as the killer of over twenty women in the United States before crossing the border into Canada, where he was captured, tried, and hanged. The evidence presented in Winnipeg in the Dark Strangler case centred on his physicality as a man perceived as mixed race. The narrative around Nelson, which was circulated several times in true crime magazines after his death, found its most famous outlet in the Hitchcock film based on the Dark Strangler case, Shadow of a Doubt. In Hitchcock’s film, the question of racial and familial identity raises its head in disquieting relationships between a mother and daughter and their serial killer relative. By examining the film through true crime paratexts, in this paper I explore how Shadow of a Doubt made taboos around illegitimacy and race visible for audiences of the 1940s.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".