Identifying the necessary components of a police decision-making model
Bibliographic record
Abstract
As police officers are entrusted with significant amounts of discretion and power in instances potentially involving arrest, use of force, search, and seizure, their decisions have serious consequences. Yet, very little research has been conducted into police officer thinking and decision-making. The objective of this research was to identify the necessary components of a decision-making model which can be used to prepare police officers to appropriately exercise their discretion when dealing with ambiguous, time-pressured, and consequential situations. The research on critical thinking (CT) and decision-making in policing was reviewed and supplemented with research from related disciplines. Multiple decision-making models were identified, discussed, and compared. The recognition/metacognition (R/M) model developed by Cohen, Freeman, and Thompson (1998) was identified as potentially adaptable for use in policing. As CT is considered best learned in domain specific environments, the police context for decision-making must be explored. A multimethod study was designed and conducted. Frontline police officers were the focus as they are particularly impacted by time, access to information, and stress effects. Responses to Critical Incident Analysis Interviews were combined with findings from the literature, to prepare a questionnaire. Canadian police services were contacted and invited to participate in a survey of frontline police decision-making. The services which agreed to participate forwarded the invitation to frontline police personnel. Respondents provided their information through an online survey. The sampling was non-random, as self-selection occurred at the service and individual levels. The results indicated that a model of police decision-making should include recognition and metacognition components taught through a domain specific approach. The five identified themes of: information, safety, planning, respite, and articulation should be used for scenario creation. A Recognition-CT Police Decision-Making Model is proposed. The information collected was detailed and rich, but cannot be confidently stated to be representative of all Canadian police officers, and while having many strengths, qualitative studies can also be prone to researcher bias. Even with these caveats, this research provides important information to improve our understanding of the complex and ambiguous environment in which police decision-making occurs. Suggestions for future research on police decision-making and the role of CT are also discussed.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".