The Role of Expert Evidence in the Defence of Provocation and Emotional Stress in South Africa
Bibliographic record
Abstract
The provocation defence has emerged as one of the most contentious defences in modern times and has remained that way for many years in jurisdictions such as South Africa, England and Canada. In South Africa, the courts have struggled in deciding what role, if any, provocation should occupy in criminal law. This dynamic approach arises from the psychological or principle-based approach to criminal liability. Provocation and emotional stress are powerful emotions. In South Africa, the criminal law recognises that these emotions may impact criminal liability by causing a temporary loss of criminal capacity. The three notorious acquittals in S v Nursingh, S v Arnold and S v Moses created controversy for the defence. In an attempt to bring clarity to this area of the law and to calm public outrage the court in Eadie effected fundamental changes in the form of a policy brake on the principles underpinning the defence. Unfortunately, this brought more confusion to the defence. However, it is submitted that the uncertain role of expert evidence in relation to this defence has arguably been a source of the problems encountered in the application of this defence. A measure of uncertainty exists regarding what, if any, the role of expert evidence plays in cases involving non-pathological incapacity due to provocation. Reform and development is needed to formulate a new approach not only to provide clarity but also to ensure improved functioning of the defence. The rules governing expert opinion evidence in respect of the defence of non-pathological incapacity are in need of review and legislative intervention.
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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 | 0.043 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.015 | 0.012 |
| 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".