Who am I? Identity and the facilitation of local youth lives within sexuality education as an HIV prevention strategy
Bibliographic record
Abstract
Sexuality education as an HIV prevention strategy is positioned as a way to empower youth in relation to their sexual identities and behaviours. While the youth subject is recognized as complex, the underlying premise is that identity can be targeted through sexuality education. In this paper, I present data from an ethnographic research study, carried out with the South African non-governmental organization loveLife, that foregrounds identity as an ongoing site of struggle within sexuality education. Data are drawn from field notes, individual semi-structured interviews and artefact analysis. Bringing together the work of educational theorist Elizabeth Ellsworth and feminist geographer Doreen Massey, I theorize the pedagogical encounter as a momentary coming together of understandings of self and other across perceived spatial and temporal boundaries. The findings problematize the possibility of a stable (and “local”) youth subject that can be targeted and empowered through sexuality education. At the same time, I argue that it is the impossibility of this project that might be a pedagogical resource for imagining social identities and their relations differently with youth.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| 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".