Sexuality, gendered identities and exclusion: the deployment of proper (hetero)sexuality within an HIV-prevention text from South Africa
Bibliographic record
Abstract
HIV prevention discourses concern lives, the protection of bodily rights and people's active involvement in the policies and programmes that affect them. HIV prevention discourses also create lives, relying upon the deployment of normative sexual identities at the same time as they invite complex and fluid youth identities to embody the norms of prevention. This paper examines a particular HIV prevention text that is available to teachers in the Western Cape province of South Africa to support the implementation of the national Life Orientation programme. Rather than considering this text as a neutral 'scaffold' upon which teachers and students add cultural meanings, it is important to interrogate the ways in which texts rely upon and reiterate particular discursive constructions of the youth sexual subject. This paper argues that the text deploys a particular discursive framework in order to construct a 'normal' (and hetero) sexuality that validates, rather than questions, social constructions of masculine privilege within heterosexuality. This is achieved through the deployment of a scientific expertise of sexuality; the mobilisation of a valued hetero/homosexual binary to create a 'safe' heterosexuality; the normalisation of bourgeois sexuality through the ideology of marriage; and the naturalisation of heterosexual masculine and feminine identities.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".