Forensic Interviewing Techniques in Child Sexual Abuse Allegations: Implications for the South African Context
Bibliographic record
Abstract
This paper aims to examine forensic interviewing techniques during child sexual abuse allegations using South African lenses. Forensic Social Work education and practice in South Africa is emerging as it has been adopted from the United States of America. There are currently no guidelines for forensic social workers to inform the assessment of children who are alleged to be sexually abused which are in a South African context. For the protection of children, skillful forensic interviews must be conducted for perpetrators of child sexual abuse to be convicted. Forensic interviews help in eliciting accurate and complete report from the alleged child victim to determine if the child has been sexually abused and if so, by whom. The ecosystems theory is used to guide this paper. An extensive literature review was conducted to zoom into systems in South Africa which influence the effectiveness of the forensic interviewing techniques useful to facilitate the disclosure of sexual abuse amongst children.
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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 | 0.052 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".