Från teori till trygghet: En komparativ studie av SafeGrowth i Drottninghög och San Romanoway
Bibliographic record
Abstract
SafeGrowth is a method in urban planning to prevent crime and promote social cohesion. The method was officially introduced in Toronto, Canada in 2001. Toronto and their application of SafeGrowth sparked global interest and in 2020, Drottninghög in Helsingborg, Sweden, decided to apply the method. They became the first city in Europe to apply SafeGrowth. This study explores the application of SafeGrowth in two specific neighborhoods, Drottninghög,Helsingborg and San Romanoway, Toronto. The application of the method involved an extensive collaboration among multiple actors. The actors included, for example, local authorities, residents and investors. Through SafeGrowths participatory approach residents were encouraged to actively engage in identifying neighborhood priorities. The method relies on a bottom-up perspective. Furthermore, this study aims to explain not only how the method was applied in the two areas but also similarities, challenges and how it affected the characteristics of Drottninghög. Two semistructured interviews, one systematic social observation and a documentanalysis were conducted. The results reveal that the two neighborhoods applied the method in a similar way but with some differences. It also suggests that the application in many ways was successful in both areas. The results also show that Drottninghög and San Romanoway face different challenges. It is clear that the application of SafeGrowth in Drottninhög affected the physical environment in a preventive way and promoted social cohesion.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".