Networked Architectures of Crime Prevention: Community Mobilization in Manitoba
Bibliographic record
Abstract
Crime prevention programs in Canada have increasingly adopted community mobilization frameworks – a process in which individuals, groups, and organizations in a community come together to address particular social issues associated with individual risk, health and safety, crime prevention, and community development. These initiatives intend to address systemic issues that are strongly correlated with criminal activity and with community safety and well-being. Twelve community mobilization (CM) initiatives have been established in Manitoba. CM is often considered an innovative way to deal with high-risk individuals who are best served by an approach that activates communities to act on their behalf and, by doing so, increases community safety. CM is also considered a networked form of crime control that activates groups not normally involved with crime control. Although intending to mobilize communities to act, some of these programs have been critiqued as being state-centric and promoting a police agenda. We have found preliminary evidence that Manitoban initiatives have avoided these problems and retained autonomy and local governance in their design and operation. Using the theoretical concept of nodal networks (organizational sites that bring together institutions to shape a flow of events), we argue that models of CM in Manitoba have maintained local leadership and resisted standardization, which gives them the potential to meet the original goals of CM: to co-produce community-grounded definitions and practices of public safety. We introduce indicators to verify these nodal networks and discuss the possibilities for reimagining public safety.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".