Citizens’ Perceptions on the Role of Court Sentencing in Crime Control: A Survey of Mthatha High Court Juridical Area in South Africa
Bibliographic record
Abstract
For over a century, the role of court sentencing on crime deterrence has generated significant debate. In this study, we explored the citizens’ perceptions on the role of court sentencing in South Africa’s Mthatha area. The findings are looked in the context of the broad theories of punishment namely: retributive theory, deterrence theory, preventive theory, reformative theory and compensation theory. A total of purposefully sampled 90 respondents were invited to participate in this study through closed-ended questionnaires. The univariate perception results of the study reveal that reformation of the offender, protection of the offender from being harmed by the victim in retaliation, and ensuring that the victims get justice are the most significant roles of court sentencing. Collectively, the reality that severe sentence scares potential criminals not to commit crime stands out and is the most correlated role of court sentencing. Court sentencing was also viewed to be having two pronged preventive effect on criminal activities. First, the criminal is incapacitated from engaging in criminal activities during the time of imprisonment; and second, the offender is removed from the environmental factors that led to offending. As part of the conclusion, the study recommends sentencing policies that mainly support reformation of offenders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".