Deadly Disease vs. Chronic Illness: Competing Understandings of HIV in the HIV Non-Disclosure Debate
Bibliographic record
Abstract
Over the past several decades, understandings of what it means to have contracted the human immunodeficiency virus (HIV) have shifted so that an infection once viewed as deadly and ultimately terminal is now largely regarded as chronic and manageable, at least in the West. Yet, the shift has not been complete. There are arenas of discourse where understandings of what health implications HIV carries with it are contested. One such space is the debate concerning the appropriate response to cases of HIV non-disclosure, that is, situations where individuals who are HIV-positive do not disclose their health status to intimate partners. This paper examines the competing constructions of HIV found within this debate, particularly as it has unfolded in Canada. Those who oppose the criminalization of non-disclosure tend to construct HIV as an infection that is chronic and manageable for those who have contracted it, not unlike diabetes. Those who support criminalization have mobilized a discourse that frames the infection as harmful and deadly. We use the case of the HIV non-disclosure debate to make the argument that representations of health conditions can become mired in larger social problems debates in ways that lead to contests over how to understand the fundamental nature of those conditions.
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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 | 0.036 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| 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".