A Script Analysis of Successful Police Interventions Involving Individuals in Crisis
Bibliographic record
Abstract
This study uses script analysis in criminology to identify steps and actions performed by police officers during their encounters with individuals in crisis to obtain their cooperation peacefully. Data were collected from 130 police reports. Descriptive and logistic regression analyses were respectively used to identify the main steps of police interventions and to estimate the effect of police actions on reactions from the person in crisis. A six-step script was identified: (1) receiving the emergency call; (2) arriving at the scene; (3) assessing the situation; (4) engaging with the person in crisis; (5) managing the situation; and (6) completing the intervention. During their interventions, officers use several techniques to obtain the cooperation of the person in crisis or de-escalate the crisis. Results indicate that support techniques (e.g., involving the person in finding a solution) lead to cooperation and permit effective de-escalation of the crisis. Conversely, individuals in crisis were less likely to cooperate or calm down when the police used nonphysical (e.g., using threats, disapproving of the person’s behavior) or physical control techniques (e.g., using constraint or intermediate weapons). Measures likely to improve police interventions with individuals in crisis are discussed, using the script analysis as a framework.
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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".