Building resilient communities in the midst of shame, guilt, fear, witchcraft, and HIV/AIDS
Bibliographic record
Abstract
One of the most challenging issues in dealing with HIV/AIDS is breaking through the stigmas surrounding the disease and building resilience in communities where large numbers of people are infected with and affected by the HIV pandemic. This article explores the relationship between shame, fear, guilt, witchcraft and HIV/AIDS stigmatization with specific reference to a rural community in South Africa. This will be done by looking at key features of the African Traditional worldview and culture. Predominant witchcraft beliefs and how it manifests in community attitudes towards PLWA as we have observed it over a period of 10 years in the KwaNdebele region will be pointed out. The influence of prevailing beliefs in witchcraft and the way it aggravates the experience of fear, shame, and suffering of stigmatization by people infected and affected by HIV will also be highlighted. Approaches to Christian HIV/AIDS counselling and intervention have to be contextualised to be culturally sensitive and relevant. At the same time, the article wants to stress that a Christian approach to HIV/AIDS intervention may be enriched and become more holistic when the aspects of the Christian Gospel dealing with God’s merciful covering of the shame of his children and Christ’s victory over and disarmament of all evil spiritual powers and authorities are believed and embraced.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".