Forensic DNA Expert Evidence in the South African Context
Bibliographic record
Abstract
Abstract Expert testimony has long played a crucial role in criminal litigation and its continued prevalence makes its trustworthiness vital to the integrity of the justice system. The criteria for accepting expert forensic testimony ensure that reliable and relevant expert opinions are presented, aiding the courts in making well-informed decisions. Unlike the jury system in the United States, South Africa’s legal system relies on a presiding officer as the fact finder and s/he may be supported by knowledgeable assessors. The expertise of the presiding officer lies in law, not forensic science, which means that the ‘fact finder’ may lack forensic science knowledge and education in forensic science. Although it is reasonable not to expect legal professionals to be experts in forensic science, this remains a limitation of the South African system. The problem is further compounded by the rare use of forensic science assessors by the presiding officer. The reliability and validity of forensic evidence depend on the adherence to established protocols derived from internationally recognised forensic standards, such as guidelines published by the Scientific Working Group on DNA Analysis Methods (SWGDAM) and forensic DNA testing laboratory standards set by the South African National Accreditation System (SANAS). Furthermore, pretrial procedures and expert consultations play a crucial role in maintaining evidentiary integrity. Cross-examination during trials further scrutinises the reliability of forensic evidence, addressing concerns raised by scientific organisations and academic communities alike. Pre-trial meetings – a key element – play a crucial role in promoting the candid flow of information, thereby enhancing the transparency and fairness of the legal process. The court’s gatekeeping role ensures the reliability and validity of forensic evidence. This article examines the legal framework in South Africa and the influence of judgments in the United States on the acceptance of forensic expert evidence.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".