Born Queer, Made Evil? Examining ‘Discovery’ and ‘Construction’ as Competing Methodologies of True Crime
Bibliographic record
Abstract
In this essay, we argue for the need to go beyond considerations of representation when conceptualising the true crime genre’s relationship to queerness and breach fundamental questions about the form of true crime. We ask: how can true crime works retrace history in a way that problematises rather than reinforces established definitions of ‘deviance,’ and this category’s historical relationship to queerness? To answer these questions, this paper outlines two competing methodologies used by true crime works like Mindhunter and American Horror Story in their respective projects of reexamining the past. Rather than considering possible methodologies for the study of true crime media, we believe that an overview of the divergent methods used by these works enables the implicit ideological investments of such texts to be made clear. First, we discuss a ‘straight’ approach that prioritises the retracing of a past crime through archival material and mimetic recreations so that a long-suppressed truth might be ‘discovered’. Second, we examine a ‘queer’ approach that freely blends fiction and historical fact to present a campy and altogether untenable version of criminality. Throughout, we argue for the utility of the latter, which serves as a productive strategy to reveal the artifice of deviancy itself.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".