Deriving Expert Knowledge of Situational Awareness in Policing: a Mixed-Methods Study
Bibliographic record
Abstract
Abstract Situational awareness (SA) is the most important skill required by police to effectively assess and respond to encounters, including critical incidents. Incomplete or sub-optimal SA strategies can lead to errors in subsequent judgement, decision-making, and action, including tactics and use of force (UOF). Errors in UOF, especially lethal force, in training or operational field settings, have severe consequences for learning, occupational health, and public safety. Therefore, adequately defining and instructing SA is an important gap to fill in existing applied police literature and practice. Using a mixed-methods approach, the current study aimed to define and conceptualize SA in police-specific contexts. Participants included 23 novice trainees and 11 experienced officers and instructors in tactics and UOF. Participants were shown 13 static images of various staged encounters, ranging from non-threatening to high-threat. Following each image, participants were interviewed and asked to describe what they saw and how they would respond. Thematic analyses of the interview data revealed the following seven themes that are highly interrelated and more completely define police-specific SA: distance/time laws; partner/roles; profiling the suspect; tactical options and opportunities; ongoing assessment of own tactical activities and outcomes; surrounding environment and conditions; and dangerous objects. Expert officers provided more detailed and multidimensional descriptions of the themes and statistical analyses confirmed that experts identified more themes compared to novices. By making tacit knowledge visible, the current findings establish a professional standard for SA formation, which can inform evidence-based police training in SA, tactical decision-making, and UOF while improving operational safety.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".