Warnings From the West: Identification and Expert Evidence as Causes of Wrongful Convictions and the Implications for South Africa (Part 1)
Bibliographic record
Abstract
Being wrongfully convicted of a crime is arguably one of the most dreadful examples of injustice in any criminal justice system. This phenomenon has been recorded and studied in Western, predominantly adversarial, jurisdictions such as the United States of America, England-Wales, Canada, and Australia since the late twentieth century. Factors that have been found to contribute to wrongful convictions include eyewitness misidentification, faulty forensic evidence, prosecutorial misconduct, inadequate defence, and many more. While adversarial safeguards such as cross-examination and acquired expertise are thought to diminish the chances of wrongful convictions by revealing unreliable evidence, an increasing number of researchers are concerned that these safeguards have little effect on accurate fact-finding in criminal trials. In South Africa, where individual reports of wrongful convictions in the predominantly adversarial criminal justice system have been recorded in the media, no systems exist to track or investigate the injustice of false convictions. The objective of Part 1 of this article is to review the existing literature on eyewitness misidentification evidence as cause of wrongful convictions as well as those adversarial safeguards upon which criminal justice systems rely to identify errors in fact-finding. Parallels are drawn to the adversarial system as it functions in South Africa, and the possibility of misidentification as root and legal cause of wrongful convictions locally is considered.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".