Framing Policing Image and Reputation: Police Engagement with Social Media as a Tool to Employ Impression Management Tactics
Bibliographic record
Abstract
Historically, policing agencies have had a great deal of control over the information released about them. Prior to social media, information was disseminated through media channels such as newspaper articles or press conferences. This allowed for the information to be carefully tailored to highlight only the positive aspects of police behaviour, which directly benefitted them. Alternatively, police used these channels to regulate the information being presented to the public to maintain the position of gatekeeper of influential information. Social media and instant technologically mediated communications offer profound novel opportunities for police to communicate with the public, but also new risks, such as, losing public confidence, legitimacy, and issues of animosity. This study utilized qualitative methods to capture how police agencies employ social media as a means to engage in impression management tactics to influence the public’s perception. In addition, the study analyzed the public’s attitudes and beliefs about the police and their views on policing as a profession through the type of interactions occurring online. The qualitative data was gathered through in-depth interviews with the communications personnel and current police officers from a police agency in Western Canada. In conjunction with the in-depth interviews, an ethnographic content analysis was performed on the social media accounts (Instagram, Twitter, and TikTok) of police agencies in Vancouver, Calgary, Edmonton, Regina, and Winnipeg. This research uncovered the impression management tactics being used to influence the public’s perception of the police. In addition, this research illuminated points of contention between the police and civilians, as well as methods for increasing positive interactions on various social networking platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".