An Analysis of the Regulatory Environment Governing Hearsay Electronic Evidence in South Africa: Suggestions for Reform – Part Two
Bibliographic record
Abstract
The purpose of this two-part article is to examine the regulatory environment governing hearsay electronic evidence in South Africa with a view to suggesting law reform in the light of the most recent proposals put forward by the South African Law Reform Commission. Part one considered the definition of data messages in the context of hearsay electronic evidence and concluded that amendment is required (as suggested by the South African Law Reform Commission). Further, part one sought to answer two additional queries posed in Discussion Paper 131 Review of the Law of Evidence in relation to electronic hearsay, ultimately finding that a data message can constitute hearsay within the meaning of the applicable legislation; further, that South African law must distinguish between data messages produced substantially by a computer or mechanical process and those that rely substantially on the credibility of a person. Part two of this article will review the statutory exceptions to the hearsay rules applicable to electronic evidence, including the controversial section 15(4) of the Electronic Communications and Transactions Act 25 of 2002. Further, part two will analyse the situation in selected foreign jurisdictions where hearsay electronic evidence has had more time to mature and develop (United Kingdom, Canada and United States) with a view to incorporating suggestions that South Africa can implement. Finally, this article will conclude by providing suggestions for law reform in the context of the recommendations put forward by the South African Law Reform Commission, and will suggest that that there must be law reform in at least the following areas: the definition of data messages; the definition of the term document in the statutes applicable to the hearsay exceptions; a distinction between types of electronic evidence insofar as computer-generated evidence with human intervention, and without human intervention is concerned; and more cohesion and alignment with the statutory hearsay exceptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".