Constructing victimhood in Canadian news coverage of HIV criminalisation: Claims‐making activities and HIV non‐disclosure
Bibliographic record
Abstract
A robust body of research has documented the representational politics of news coverage in their depiction of HIV-positive people charged for HIV non-disclosure. News media representations of HIV-negative sex partners in cases of HIV non-disclosure have received far less scholarly attention. Adopting a social constructionist perspective, this article identifies how "victims" of HIV non-disclosure are constructed in news media. It is based on a dataset consisting of 341 news articles on HIV non-disclosure from 14 English Canadian newspapers across the political spectrum. Victims of HIV non-disclosure were constructed as: (1) suffering horribly, (2) morally pure and virtuous, (3) vengeful and (4) agentic and responsible for their situation. We consider how such constructions are enmeshed within arguments that establish or reject HIV non-disclosure as a social problem. We then discuss the ways these constructions and the assumptions upon which they are based reflect broader discussions on the severity of HIV, the responsibility for HIV risk and exposure, and the contestations over the very nature of the social problem of HIV non-disclosure. Constructions of victims that uphold HIV criminalisation have relied on assumptions of HIV as a deadly disease but de-emphasise personal responsibility for HIV risk. By contrast, constructions of victims that, in effect, oppose HIV criminalisation have tended to minimise the harms of HIV and invoke personal responsibility for HIV risk. We suggest that both proponents and opponents of HIV criminalisation engage in the "ideology of victimhood" and thus participate in and reinforce what Best (1997) termed, the "victim industry."
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".