CRIME DOES NOT CAUSE PUNISHMENT: The impact of sentencing policy on levels of crime
Bibliographic record
Abstract
If countries can learn from one another, South Africa can learn from the experiences of other countries that have re-organised their sentencing systems in recent decades. South Africa’s correctional system has many similarities to America’s – seriously overcrowded prisons, sentences that are too long, stark disparities, and therefore injustices, in sentences received for comparable crimes. American solutions – mandatory minimums, prison terms measured in decades not years – have neither reduced crime rates nor made streets safer. Nor will they in South Africa. Comparisons of countries with very different sentencing policies and punishment practices – Canada versus the United States, Finland versus the rest of Scandinavia, England versus Scotland – show that sentencing and punishment have little discernible effect on crime trends and patterns. Crime trends and patterns in most developed countries move in broad parallel, irrespective of national punishment policies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; 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".