"We Have Our Own Special Language." Language, Sexuality and HIV/AIDS: A\nCase Study of Youth in an Urban Township in South Africa.
Bibliographic record
Abstract
Background: Despite the fact that most South African youth know about HIV / AIDS and how it can be prevented, there is a high prevalence of HIV / AIDS amongst youth in South Africa.Generally youth do not practice safe sex, and youth sexuality is characterised by multiple sexual partners, not using condoms and transactional sex.Objectives: To minimize the risk of HIV infection, it is necessary to understand youth sexuality.In this paper I explore youth sexuality with a specific focus on how language influences sexuality.Methods: I use discourse analysis and qualitative research techniques.Purposive sampling, a form of non-probability sampling was used.I interviewed seventy youth individually or in groups and used in-depth semi-structured interviews.Results: The use of language influences youth sexuality.Youth have developed a specialised language to talk about sex and sexuality and this language has become part of the daily discourse, so that unsafe sexual practices become norms and are justified.Conclusions: The realm of language can be a creative way for peer and HIV / AIDS educators to work with youth towards creating a healthier sexuality.However, as language always occurs in a material context, it is also necessary to work towards changing the material environment, such as poverty.This environment not only facilitates the development of a particular language but it also encourages unsafe sexual practices such as transactional sex.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".