Examining the Impact of Body Worn Cameras on Police Officer Memory Following a Critical Incident
Bibliographic record
Abstract
Numerous high-profile events have contributed to an increase in public scrutiny of police performance; this has resulted in many agencies considering the use of body worn cameras (BWCs).However, some research suggests that having access to saved information can have a negative effect on memory, particularly when an individual experiences stress during the recorded event.Therefore, the aim of the current research was to: (1) examine how BWCs impact the quality of an officer's encoding and retrieval of event details, (2) determine the benefits and challenges associated with officers viewing BWC footage when writing their reports, and (3) attain a better understanding of the efficacy of cognitive interviewing (CI) as a means to enhance memory retrieval of use-of-force events.More specifically, Study 1 examined the recall of officers who were aware that they could "offload" memory encoding tasks to their BWC.Interestingly, regardless of whether officers believed they could rely on a camera, or not, a similar amount of information was recalled.In Study 2, differences between officers who provided a statement before watching their BWC footage were compared to officers shown their footage first.The findings indicated that officers allowed to preview their footage before reporting included more details about the subject in their statement.Moreover, higher levels of stress were associated with an increase in the amount of moderate and major errors made by officers who were barred from seeing their footage.Lastly, the goal of Study 3 was to investigate the efficacy of the CI as a means to improve recall.The comparison found that officers recalled more information and confabulated less information if they completed a CI.Broadly speaking, the current research answers important questions about the impact of BWCs on officer's ability to recall use-of-force events; thereby adding to the cognitive BODY WORN CAMERAS AND MEMORY iii psychological literature, as well as the legal field.The research may also have an applied impact namely by: (1) assisting police agencies in developing more informed policies around BWCs, and (2) by educating police officers and juries about the impact of BWCs on memory to ensure the critical appraisal of this controversial technology.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".