Emotional storytelling: Sensational media and the creation of the HIV sexual predator
Bibliographic record
Abstract
More than 180 people in Canada have faced criminal charges related to HIV nondisclosure. Media coverage is often sensational and commonly portrays people living with HIV as hypersexualized threats to the (inter)national body politic. This article analyzes mainstream news media coverage of four HIV nondisclosure cases to examine how the accused (two men, two women) are constructed as sexual predators, which we found occurs through two key discursive moves. First, by tying the narrative to stereotypical conceptualizations of hegemonic and toxic masculinity and pariah femininity to construct the individual as promiscuous, hypersexual and dangerous. Second, by crafting a narrative that evokes complex moral emotions; notably, these include the ‘negative’ emotions of anger, disgust and fear. Given that racialized men are disproportionately represented and demonized in media accounts, and the tense race relations in the current western political landscape, it is important to consider how emotions (rather than medical evidence of the risks of transmission, intent to infect or actual transmission) might contribute to shaping punitive mentalities and the harsh application of the law. By examining how race, gender, class and sexuality are mobilized to construct narratives of Black masculinity as inherently toxic and women’s sexual freedom as exemplifying pariah femininity, and the ways in which the coverage evokes negative moral emotions, we contend that media coverage shores up moralized discourses about sexuality, masculinity and femininity and HIV/AIDS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".