Deciphering Dangerousness: A Critical Analysis of Section 286A and B of the Criminal Procedure Act 51 of 1977
Bibliographic record
Abstract
The violent nature of some crimes and the high crime rate in South Africa reflect the fact that some offenders constitute a real threat to the security of communities. It is understandable, therefore, that the state seeks to protect its citizens through preventive measures. Although South Africa has certain legal provisions on its statutory books, it seems that the declaration of persons as dangerous criminals is under-utilised. South African legislation dealing with the declaration of dangerous criminals can be improved by borrowing some traits of the Canadian legislation. Such features include the restriction of courts' discretion and the provision of concrete and more detailed guidelines on the nature of the offences for which the provision can be applied. The courts could also take into account the type of criminal history of the offender which would merit the declaration of a dangerous criminal. It is also important that the extent of the violence in an offence should be thoroughly defined in court. Courts need to balance their wide discretion on the matter with the provisions in the Act in order to protect the community against dangerous criminals.
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 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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| 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".