Using multi-agency, multi-professional collaboration to reduce serious violence and organized crime
Bibliographic record
Abstract
Serious violence and organized crime have been rising both nationally and in Sheffield, contributing significantly to increasing knife and gun crime, which results in threats to community safety and well-being. A multi-agency project with stakeholders across all levels of command and co- located operational staff was established to undertake collaborative activity that would protect the public by pursuing offenders as well as preparing for and preventing serious violence and organized crime: Fortify. Using a 4P approach, Fortify worked across professional and organizational boundaries to disrupt serious violence and organized crime. Relationships between partners have improved substantially through increased communication and understanding of the different roles, perspectives, and levers of each partner. A recent Home Office locality review applauded our partnership. Intelligence sharing has improved, leading to increased disruptive activity, including increased seizure of money, drugs, and firearms, as well as more arrests and safeguarding referrals. The number of mapped Organized Crime Groups (OCGs) operating across the city has reduced from 19 to 12. Processes and procedures have improved, reducing duplication and holding of information in silos. Community groups are more engaged, allowing us to address serious violence and organized crime in partnership. We propose to undertake action research with the involvement of all partners to provide more robust evaluation of our initial findings. We have found that collaboration between Police and Partners increases collective responsibility and facilitates success in tackling serious violence and organized crime.
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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".