1 Changing Perceptions of Opportunities: Hope for Young People in High HIV-Risk Environments
Bibliographic record
Abstract
2 The sense of being trapped at the bottom of the pile- unable to navigate an acceptable future-predisposes to risky sexual behaviour.1 This paper asks how strategies that to engender hope can help to prevent HIV infection among young people in a heavily infected and socio-economically polarized country like South Africa. Meta-analysis of behaviour change strategies for HIV prevention among young people shows that fear-based approaches are least effective, while those that are motivational – fostering hope and opportunity and building self-efficacy – have more impact.2 But it is known whether this holds in contexts in which, for the majority, day-to-day choice and opportunity are severely constrained, and prospects of real improvement are poor? Much, though not all, of the literature linking optimism to safer sexual behaviour describes interventions in relatively low HIV prevalence environments or on islands of poverty in a sea of wealth.3 How far can hope take young people when a quarter of 24-year-old women are HIV positive4, three-fifths of the population earn less than a fifth of total income,5 and only a third of 18-35-year-olds have ever had a job?6
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".