Factors Influencing Non-Disclosure of Child Sexual Abuse Amongst Bapedi Tribe in Polokwane, Limpopo Province, South Africa
Bibliographic record
Abstract
Child sexual abuse is a global public health issue calling the attention of practitioners, scholars and policy makers to address it. This study argues that children are being sexually abused both by family and non-family members, and such incidents are not always reported and/or disclosed due to various influential factors. This study was aimed at exploring and describing possible influential factors for the non-disclosure of child sexual abuse amongst Bapedi tribe. A qualitative approach with a descriptive phenomenological design was followed. Fifteen caregivers of children were purposively selected in order to pursue the aim of this study. Data was collected through semi-structured interviews and analysed thematically through the help of Nvivo Software. The findings indicate that due to factors such as fear of the perpetrator, the practice of the spirit of ubuntu, socio-economic status of the family and relationship with the perpetrator, protecting the dignity of the family, fear of victimisation, fear of witchcraft, and cultural beliefs, child sexual abuse in the Bapedi tribe is an issue to be dealt with by families affected and if need be, traditional courts intervene in case the families disagree. It can therefore be concluded that there is a lack of information on the nastiness of child sexual offences against children amongst Bapedi tribe. Therefore, the Bapedi tribe must be empowered to disclose and/or report child sexual abuse and the implications of not doing so.
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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 | 0.001 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".