How Do Police Respond to Stalking? An Examination of the Risk Management Strategies and Tactics Used in a Specialized Anti-Stalking Law Enforcement Unit
Bibliographic record
Abstract
How do police respond to and manage complaints of stalking? To answer this question, we conducted a 3-phase study. First, we reviewed the literature to identify risk management tactics used to combat stalking. Second, we asked a group of police officers to review those tactics for completeness and group them into categories reflecting more general risk management strategies. The result was 22 categories of strategies. Finally, we used qualitative methods to evaluate the files of 32 cases referred to the specialized anti-stalking unit of a metropolitan police department. We coded specific risk management tactics and strategies used by police. Results indicated that a median number of 19 specific tactics from 7 general strategies were used to manage risk. Also, the implementation of strategies and tactics reflected specific characteristics of the cases (e.g., perpetrator risk factors, victim vulnerability factors), suggesting that the risk management decisions made by police were indeed strategic in nature. Qualitative analyses indicated that some of the strategies and tactics were more effective than others. We discuss how these findings can be used to understand and use stalking risk management more generally, as well as improve research on the efficacy of risk assessment and management for stalking.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".