Bibliographic record
Abstract
This article puts forward an argument for the importance of HIV/AIDS to digital studies, focusing, focusing on the North American context. Tracing conjoined histories and presents makes clear that an HIV-informed approach to digital media studies offers methods for attuning to marginalized media practices that should be central to interrogating the politics, relations, and aesthetics of digital media. Artist Kia LaBeija’s #Undetectable (2016) is closely analyzed in order to explicate some of HIV’s potential resonances for digital studies, including viral media and justice-based responses to surveillance. We then propose a methodological framework for centering HIV in understandings of three key concepts for the field: (1) networks; (2) social media and platforms; and, (3) digital history. We argue that HIV-positive users bring expertise to navigating digital infrastructures that can surveil and harm while also facilitating pleasure and connection. Such tension provides models of response that publics need to insist upon more just digital tools and structures for our unfolding present.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.093 |
| Scholarly communication | 0.027 | 0.031 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".