Young People, Social Media and Exposure to STIs: A Semi-ethnographic Experiment
Bibliographic record
Abstract
Abstract A decade ago, the AIDS pandemic was driven by determinants such as poverty, deprivation, migrancy, patriarchy and gender-based violence. Today, however, the socio-economic and structural drivers of HIV infections have assumed or added other dimensions, including social and electronic media and reality television. These new dimensions saw further expression with the advent of the COVID-19 pandemic from 2020 onwards. To consign both HIV and the COVID-19 pandemics to history’s museum of pandemics, strategists must employ greater infiltration and mastery of social and electronic media and reality TV. In the case of HIV, these created social clouds or bubbles where unprotected sex, transactional sex and multiple concurrent sexual partnerships are manufactured and proliferated globally. The same was the case with the COVID-19 pandemic, in which case these social clouds or bubbles created an alternative narrative about the source of the pandemic, who and how people get infected, and both the requisite remedies and preventions in this regard. With reality television gaining popularity on low-cost paid channels and free-to-air television; with smartphone penetration widening and costs of access to data falling, a social cloud has been created, enabling the cultural majority (those who control the media and capital) to set trends for everyone, including those with less means. These trends in turn become a standard many aspire to live by. The ontological density of the poor and lower middle-class women is lost through the universalisation of social and cultural trends set by middle elites who control the production and reproduction of knowledge and shape international and national imagination. It is these discourses, and their shaping of imagination as a consequence, that this chapter deals with. It looks at both the implications and consequences which, in the case of pandemics such as these, can be dire.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".