Bibliographic record
Abstract
Abstract This paper argues that scholars of computing, networks, and infrastructures must reckon with the inseparability of “viral” discourses in the 1990s. This co-assembled history documents the reliance on viral analogies and explanations honed in the HIV/AIDS crisis and its massive loss of life, widespread institutional neglect, and comprehensive technological failures. As the 1990s marked a period of intense domestication of computing technologies in the global North, we document how public figures, computer experts, activists, academics, and artists used the intertwined discourses surrounding HIV and new computer technologies to explicate the risks of vulnerability in complex, networked systems. The efficacy of HIV as an analogy is visible in the circulation of viral concepts, fears surrounding interdependence, and emergent descriptions of precarity in the face of a widespread “infrastructure crisis.” Through an analysis of this decade, we show how HIV/AIDS discourses indelibly marked the domestication of computing, computer networks, and nested, digitized infrastructures.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.063 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".