Bibliographic record
Abstract
Crime prevention work in Australia is notable for significant innovation and achievement in a number of important areas. However, the ability to consolidate these successes has been hampered by a number of structural factors, including continuing fragmentation between the state/territory level and the national bodies; a lack of strong national leadership and a shared vision for crime prevention goals; frequent changes in direction and strategic priorities across all levels of government; short-term arrangements that shift from "project" to "program" level; a lack of cohesion and coordination between key agencies (particularly police); and the absence of an adequate evidence base to support the dominant strategic approach - the community-based crime prevention model. This article discusses each of these issues from the perspective of managing crime prevention work at the various levels of Australian government and offers some thoughts on possible future directions and methods for overcoming existing shortcomings. Particular attention is paid to the impact of the increasing commitment to the use of "whole of government" models for developing and implementing crime prevention work, the emergence of the "urban renewal" model as a framework for broadening and strengthening the community-based crime prevention approach, the changing role of police in crime prevention, and the importance of building adequate evidence bases to support crime prevention practice.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".