Studying the “sexuality-health-technology nexus”: a new materialist visual methodology
Bibliographic record
Abstract
A school of critical sexual health scholars argues that biomedical and digital technologies need to be understood not as mere objects of use, but as having the agentic capacity to effect new senses of the self and transform social/sexual health relations and outcomes. Such a call to grapple with the multidimensionality of technologies, their affects and effects poses a challenge to current methodological frameworks. To address this challenge, we introduce a novel visual methodology called "embodied mapping" that builds on the arts-based method of body mapping. Drawing from new materialism scholarship, embodied mapping extends the scope of inquiry of sexual-health research and conventional qualitative methods. It does so by interrogating the capacities and properties of sexual agents, technologies and readily available discourses on sexual health and HIV prevention as co-constitutive within the sexual-health-technologies nexus itself. Embodied mapping's research process is collaborative and emergent; researchers, together with an artist and research participants co-create a visual collage tracing the thick moments of sexual/health encounters. Embodied mapping's methodological and analytical capacity to approach sexual health phenomena as performative and immanent to the research process could open new sight lines for comprehending and intervening in this globalised era marked by an increasing technologising of sexual health care.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".