Community engagement ‘completes the puzzle’: the significance and meaning of community engagement to officers
Bibliographic record
Abstract
Research on community engagement largely focuses on the potential benefits of such activities for police services, policing as an industry, and the larger community, such as reduced disorder and anti-social behavior as well as increased confidence and trust in police. Absent from this conversation, however, are the officers who voluntarily initiate or participate in community engagement and the significance of these activities to them. To address this gap, this study examines officers’ engagement in community initiatives and what those activities mean to them. This study uses qualitative data from semi-structured interviews with 26 officers of various ranks who voluntarily initiated and/or participated in a community-based program(s) or event(s). A thematic analysis of the data reveals three overarching themes on what community engagement activities bring to or mean to officers: (1) community engagement represents an opportunity to build relationships with community members; (2) community engagement is a welcomed opportunity to feel like officers are helping the community; and (3) community activities contribute to officers’ mental wellbeing. This study demonstrates that community engagement is significant and meaningful to the individual officers involved. Thus, not only does community engagement benefit the police service and community (as per the existing literature), it also has potential benefits for officers.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".