“Tough on crime” reforms: What psychology has to say about the recent and proposed justice policy in Canada.
Bibliographic record
Abstract
The direction of recent and proposed justice policy in Canada is characterised by more criminal offences and longer periods of incarceration. This policy is based on the rationale that crime in Canada is increasing and the perception that Canadians are not safe. This article reviews whether there is empirical support for the rationale of this policy and the related assumption that this policy will reduce crime and better protect the public. From the existing literature, it seems clear that (1) crime is not on the increase in Canada, (2) it is unlikely that the reforms will lower crime rates, and (3) there is a large financial and human cost of the recent and proposed criminal justice policies. We conclude that “tough on crime” policies are not supported by the scientific literature (e.g., Smith, Goggin & Gendreau, 2002; Stalhlskopf, Males, & Macallier, 2010). Psychology would argue for evidenced-based justice policy; specifically, that early intervention, prevention, and rehabilitation are more beneficial in reducing crime in the long-term and more cost-effective (e.g., Aos, Phipps, Barnoski, & Lieb, 2001; CPPRG, 2010). Policy implications from the field of psychology are discussed.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".