Tracing trends : the impact of science and technology on the law of criminal evidence and procedure
Bibliographic record
Abstract
This article is a reflection on how science and technology have already influenced the law of evidence and procedure, and how they might well further influence it in the future. I focus on whether, and to what extent, expert evidence is changing the way criminal cases are proved and evaluated and what general trends can be detected in this area. An example of this can be seen in the increased use of different forms of forensic identification evidence in the criminal justice process. This development has affected all stages of the criminal justice process, revolutionising investigative techniques and evidence collection, adjusting pre-trial procedures, and transforming the presentation of evidence both at trial as well as during the evaluation of this information in judgments. My observations are based on research which relates to developments in adversarial jurisdictions such as the United States, Canada, England and Wales and Australia. The article highlights how changes have come about and how they are being managed in these legal systems. Against this background this article attempts to ascertain the extent to which South African law mirrors the trends in these jurisdictions. The growing scientific and technological world we live in has created certain issues that are universal challenges to all legal systems. This article also demonstrates that the law of evidence has become more multidisciplinary and interdisciplinary. The world-renowned scholar of evidence and proof, William Twining, has always claimed that 'the law of evidence is a multi-disciplinary field' in its own right. Expert evidence demonstrates progressively that lawyers need to know not only the law, but must also be familiar with the fields of science and technology which are nowadays increasingly being used to provide evidence in litigation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".