Social media and policing: matching the message to the audience
Bibliographic record
Abstract
Purpose This research aimed to explore the characteristics of respondents who accessed a municipal police service's webpage or social media (Facebook or Twitter). Perceptions about the usefulness of social media in policing were solicited from the respondents. Design/methodology/approach Several survey items about social media were included in a study of trust and confidence in policing that was collected in two waves: a random telephone sample of 504 community residents and 314 university students. Findings One in five respondents had accessed the police service's webpage, while 6.9 percent had accessed their Twitter feed and 5.4 percent had viewed their Facebook site. Social media users tended to be younger and better educated while respondents over 65 years of age rarely accessed these tools. Younger respondents reported that computer‐based methods of communication were useful whether they had accessed these services or not. Older non‐users, by contrast, saw little future value in social media. Chi‐square analyses revealed that users of social media had more confidence in the police as well as greater overall satisfaction with the police. Research limitations/implications Participants were from a medium‐sized Canadian city and the results might not be generalizable to other populations. Practical implications Social media campaigns should be planned and target demographic groups likely to receive the intended message. Younger and better educated residents are the highest users of these services. Computer‐based media campaigns targeting senior citizens will likely be ineffective given their low participation in accessing social media and lack of interest in these methods of communication. Originality/value This study is one of the first to examine the recipients of social media and their perceptions of the usefulness of computer‐based communication for law enforcement.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".