Lessons for Canadian Crime Prevention: Cultural Shifts and Local Flexibilities
Bibliographic record
Abstract
This volume of the IPC Review contains two very significant articles, written from the privileged position of hindsight by two very skilled observers. Peter Homel’s Lessons for Canadian crime prevention from recent international experience and Enver Solomon’s New Labour and crime prevention in England and Wales: What worked? offer a wealth of experience and advice based primarily on the recent history of criminal justice and prevention initiatives in Australia and England and Wales. In 2008, ICPC published its first International Report on Crime Prevention & Community Safety1, providing an opportunity to assess the evolution, maturation and growth of crime prevention internationally. These articles offer some valuable detail and commentary on some of the international trends identified in that report. Enver Solomon is a political scientist whose analysis draws on his recent “independent audits ” of ten years of criminal justice and youth justice reforms in England and Wales, under Tony Blair’s Labour government. Peter Homel has the dual distinction of having undertaken a major evaluation of the Crime Reduction Programme in England and Wales, which formed a crucial part of Tony Blair’s crime strategy, and of evaluating and observing many of Australia’s recent crime prevention initiatives, as well as some of those in New Zealand and the US. This enables him to reflect on the comparative advantages and disadvantages of central government intervention in crime and its prevention. In the late 1990’s England and Wales was seen as a poster child for crime prevention in place of “endless law enforcement”. The enactment of mandatory
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".