‘When you own the time, it’s seamless, but when you don’t, it’s horrific’: critical, public order and major incident decision-making in policing
Bibliographic record
Abstract
On 24 May 2022, a gunman entered an elementary school in Uvalde, Texas and fatally shot 19 victims. During the incident, local police were present at the site but did not enter the building to confront the gunman. It was subsequently reported that an Incident Commander mischaracterised the situation as a ‘barricaded subject’ rather than as an ‘active shooter’, and officers were ordered to stay out of the scene and to keep student families and bystanders from entering the building. Drawing on qualitative interviews with Incident Commanders, Critical Incident Commanders, Emergency Planners, and Tactical and Public Order Unit personnel, our research seeks to better understand the influence of various stressors on decision-making in high-risk, high-profile events. This paper presents an analysis of these interviews that considers four important questions: 1. Who becomes an IC? 2. What is the function of an IC during a major, critical, and public order incident? 3. What factors influence IC decision-making? and 4. What are the sources of stress involved in IC decision-making and work?
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.025 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".