Bibliographic record
Abstract
There is great interest in what testing, pharmaceutical, information and social media technology can do for sexual health. Much programmatic and research activity is focused on assessing how these technologies can be used to best effect. Less obvious are analyses that place technology into historical, political and real-world settings. Developing an 'in-context' analysis of sexual health technology, this paper draws on interviews with leading community advocates, researchers and clinicians in Australia, Canada and the UK and looks across examples, including social media, rapid HIV testing, pre-Exposure Prophylaxis for HIV and polymerase chain reaction Chlamydia testing. The analysis is framed by studies of techno-society and the dialectics of sex-affirmative advocacy with biomedical authority and attends to: the rationalistic and affective dimensions of the imaginary associated with technology; the role of technology in the re-spatialisation and re-temporalisation of the sexual health clinic; and the re-invention of technology in its real-world contexts. This in-context approach is important for: the effective implementation of new technology; strengthening the social science contribution to the field; and enriching social theory in general on life in techno-societies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".