Virtual friction: Networking sexuality and HIV prevention in the digital age
Bibliographic record
Abstract
From advances in HIV prevention science bringing us pre-exposure prophylaxis (PrEP) to the proliferation of hook-up apps like Grindr, the late 20th/early 21st centuries have introduced intense socio-technical transformations in gay men’s intimate lives. In particular, the networked decentralization and privatization of sexuality has generated a corresponding set of discourses within gay men’s communities and in the social world of HIV prevention. Community narratives either construct the Internet as a virtual community where acceptance, solidarity, friendship, romance, and sex become easily accessible in a largely hetero-normative world, or a virtual bathhouse accelerating the depoliticization and commodification of gay life (Kapp, 2011; Ward & Arsenault, 2012). In public health, accounts oscillate between exploring the Internet’s potential to revitalize HIV prevention efforts (Chiasson et al., 2009; Rhodes et al., 2011; Rosser et al., 2010), and debating its possible role in facilitating HIV risk and transmission (Berry et al., 2008; Bull & McFarlane, 2000; Wohlfeiler & Potterat, 2005). Intersecting perspectives from communication, Internet studies, and public health, this dissertation traces the erotic and epidemiological contours of a “network society” (Castells, 1996) where the Internet plays an ambivalent role in social life. Based on archival research, personal experience, and 31 interviews with gay men, public health actors, and Internet entrepreneurs in San Francisco and Vancouver, this project uses the concept of virtual friction to think through the tensions, contradictions, and paradoxes that characterize the networking of sexuality and HIV prevention in the digital age. Broadly speaking, I ask whether and how the Internet has transformed sexuality and HIV prevention by examining the discourses, subjectivities, and practices that have emerged, as well as the subsequent set of opportunities and challenges they generate for the various social worlds involved (Strauss, 1978). I argue that virtual friction is not only an inevitable but necessary part of the process because it renders visible the limits of imagining social problems and solutions in purely technological terms. Friction challenges us to acknowledge the competing epistemologies, interests, and perspectives that underpin life in the digital age, taking us out of our comfort zones by asking how we know and believe what we do about science, technology and society.
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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 | 0.002 | 0.004 |
| 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 | 0.012 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".