Action on Crime Prevention: A Multimedia Profile of NCPC Pilot Projects By
Bibliographic record
Abstract
The notion of “crime prevention through social development ” recognizes that complex social, economic, and cultural processes contribute to crime and victimization. Therefore, in addition to criminal justice policies and programs related to crime control, governments support the safe and secure development of individuals, families and communities. The Government of Canada does so through the National Strategy on Community Safety and Crime Prevention, which is administered by the Department of Justice and the Solicitor General of Canada via the National Crime Prevention Centre (NCPC). The National Strategy concentrates on crime prevention through social development; thus the NCPC supports social development projects that reduce crime and victimization in communities across Canada. More than 2,000 pilot projects have been supported since 1998. As part of the Action on Crime Prevention: A Multimedia Profile of NCPC Pilot Projects, this CPRN Discussion Paper describes how social development approaches to crime prevention fit into broader societal efforts to control crime and limit victimization. This is an important issue for the Family Network of CPRN, which is dedicated to advancing public debate on a full range of policy issues that have an impact on Canadian families of all types and on the circumstances
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".