Counting Recidivism, and Achieving Sentencing Goals: A comparative study of the Canadian and Australian criminal justice systems with policy reform recommendations
Bibliographic record
Abstract
When comparing recidivism rates between Canada (23.4%) and Australia (54.9%), Canada comes across as faring well above Australia in rehabilitation and deterring crime. This leads to the question, is Canada counting recidivism in a way that allows us to achieve our policy goals? Through a literature review, data is compiled on what recidivism data measures, sentencing, and rehabilitation programs – education and employment, all in relation to their impact on recidivism. The purpose of this paper is not only to compare Canadian and Australian measures of recidivism, but the countries’ criminal justice policies. In this sense it is a comparative study of the downstream effects of different approaches to measuring recidivism, sentencing and rehabilitation. The rate of recidivism, which is often referred to as the rate of return, is merely the count of how many individuals continue to commit crime after their initial offence. It is difficult to study recidivism comparatively due to the various nuances when measuring and counting recidivism across jurisdictions. This report will look at methods to count recidivism, sentencing, and rehabilitation programs.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".