Public police’s philanthropy and Twitter communications in Canada
Bibliographic record
Abstract
Purpose Police services, police associations and police foundations now engage in philanthropy and these efforts are communicated using social media. This paper examines social media framing of the philanthropic and charitable work of police in Canada. Design/methodology/approach Drawing from discourse and semiotic analyses, the authors examined the ways that police communications frame contributions to charity and community’s well-being. Tweets were analyzed for themes, hashtags and images that conveyed the philanthropic work of police services, police associations as well as police foundations. Findings The authors discovered four main forms of framing in these social media communications, focusing on community, diversity, youth and crime prevention. The authors argue that police used these communications as mechanisms to flaunt social capital and to boost perceptions of legitimacy and benevolence. Research limitations/implications More analyses are needed to examine such representations over time and in multiple jurisdictions. Practical implications Examining police communications about philanthropy not only reveals insights about the politics of giving but also the political use of social media by police. Originality/value Social media is used by organizations to position themselves in social networks. The increased use of social media by police, for promoting philanthropic work, is political in the sense that it aims to bolster a sense of legitimacy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".