Transitioning from pandemic to normalcy: what police departments can learn from the rank-and-file
Bibliographic record
Abstract
Purpose After more than 18 months of life during a pandemic, much of the world is beginning to transition back to some semblance of normalcy. As that happens, institutions – including policing – need to acknowledge changes that had been made during the pandemic and decide what modifications and innovations, if any, to continue moving forward. Design/methodology/approach The authors use semi-structured interviews and focus groups of police personnel in the United States (US) and Canada. The sample includes police officers and frontline supervisors (n = 20). The authors conduct qualitative analysis using deductive and inductive coding schemes. Findings The sample identified four areas of adaptation during the pandemic: 1) safety measures, 2) personnel reallocation, 3) impacts on training and 4) innovation and role adjustments. These areas of adaptation prompted several recommendations for transitioning police agencies out of the pandemic. Originality/value A growing number of studies are addressing police responses to the pandemic. Virtually all are quantitative in nature, including all studies investigating the perceptions of police personnel. The body of perceptual studies is extraordinarily small and primarily focuses on police executives, ignoring the views of the rank-and-file who are doing the work of street-level police business. This is the first study to delve into the perceptions of this group, and does so using a qualitative approach that permits a richer understanding of the nuances of perception.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".