Interpretation of the SafeGrowth method from a police perspective—Possibilities and hindrances in local crime prevention initiatives
Bibliographic record
Abstract
In this article, the focus is on the crime prevention method SafeGrowth and its implementation in Drottninghög, Sweden. We highlight the police perspective on the implementation of SafeGrowth in Drottninghög, a risk area in Helsingborg, Sweden. Contrary to ordinary crime prevention programmes, the police are not the leading actors in the SafeGrowth process; instead, they join, as an equal party, the residents and other actors in the project. This article is partly the result of a process evaluation conducted between August 2021 and October 2022. The data consists of a focus group interview and an on-site visit and was compiled in October 2022. In the analysis, three themes related to the police perspective are identified: (1) the relationship between SafeGrowth, the area’s crime problem, and evidence-based policing; (2) the contribution of SafeGrowth in terms of collective efficacy, and (3) problems related to evaluating SafeGrowth within the area. In the results, it becomes clear that, from a police perspective, the implementation of SafeGrowth may become problematic. The problems pertain to the selection of both areas and local problems to work with, the conjunction of different descriptions of realities, and organization within the project. For SafeGrowth to succeed in Drottninghög and similar areas, police perspectives must be included more clearly in order to facilitate cooperation. Despite these problems, we identified that a major advantage of the SafeGrowth method was its contribution to collective efficacy in the area, which, in turn, can be helpful to everyday police work in Drottninghög.
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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 | 0.047 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.021 | 0.056 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".